{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_data():\n",
    "    order = pd.read_csv('order.csv')\n",
    "    customer = pd.read_csv('customer.csv')\n",
    "    date = pd.read_csv('date.csv')\n",
    "    product = pd.read_csv('product.csv')\n",
    "    return order, customer, date, product"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "order, customer, date, product = get_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>订单日期</th>\n",
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       "      <td>1699.0</td>\n",
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       "  </tbody>\n",
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       "<p>60398 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            订单日期    年份  订单数量  产品ID     客户ID  交易类型  销售区域ID 销售大区  国家    区域 产品类别  \\\n",
       "0       2016/1/1  2016     1   528  14432BA     1       4  西南区  中国  大中华区   配件   \n",
       "1       2016/1/2  2016     1   528  18741BA     1       4  西南区  中国  大中华区   配件   \n",
       "2       2016/1/2  2016     1   528  27988BA     1       4  西南区  中国  大中华区   配件   \n",
       "3       2016/1/5  2016     1   528  25710BA     1       4  西南区  中国  大中华区   配件   \n",
       "4       2016/1/6  2016     1   528  14999BA     1       4  西南区  中国  大中华区   配件   \n",
       "...          ...   ...   ...   ...      ...   ...     ...  ...  ..   ...  ...   \n",
       "60393  2016/7/28  2016     1   528  27198BA     1       6   韩国  韩国    韩国   配件   \n",
       "60394  2016/7/29  2016     1   528  27087BA     1       6   韩国  韩国    韩国   配件   \n",
       "60395  2016/7/29  2016     1   528  23385BA     1       6   韩国  韩国    韩国   配件   \n",
       "60396  2016/7/30  2016     1   528  15444BA     1       6   韩国  韩国    韩国   配件   \n",
       "60397  2016/7/30  2016     1   528  15196BA     1       6   韩国  韩国    韩国   配件   \n",
       "\n",
       "                                产品型号名称  产品名称   产品成本      利润      单价    销售金额  \n",
       "0      Rawlings Heart of THE Hide-11.5  棒球手套  500.0  1199.0  1699.0  1699.0  \n",
       "1      Rawlings Heart of THE Hide-11.5  棒球手套  500.0  1199.0  1699.0  1699.0  \n",
       "2      Rawlings Heart of THE Hide-11.5  棒球手套  500.0  1199.0  1699.0  1699.0  \n",
       "3      Rawlings Heart of THE Hide-11.5  棒球手套  500.0  1199.0  1699.0  1699.0  \n",
       "4      Rawlings Heart of THE Hide-11.5  棒球手套  500.0  1199.0  1699.0  1699.0  \n",
       "...                                ...   ...    ...     ...     ...     ...  \n",
       "60393  Rawlings Heart of THE Hide-11.5  棒球手套  500.0  1199.0  1699.0  1699.0  \n",
       "60394  Rawlings Heart of THE Hide-11.5  棒球手套  500.0  1199.0  1699.0  1699.0  \n",
       "60395  Rawlings Heart of THE Hide-11.5  棒球手套  500.0  1199.0  1699.0  1699.0  \n",
       "60396  Rawlings Heart of THE Hide-11.5  棒球手套  500.0  1199.0  1699.0  1699.0  \n",
       "60397  Rawlings Heart of THE Hide-11.5  棒球手套  500.0  1199.0  1699.0  1699.0  \n",
       "\n",
       "[60398 rows x 17 columns]"
      ]
     },
     "execution_count": 4,
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    }
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    "order"
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  {
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       "          客户ID\n",
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  {
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   "execution_count": 6,
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       "      <th>1456</th>\n",
       "      <td>2016/1/27</td>\n",
       "      <td>2016</td>\n",
       "      <td>Q1</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>2016Q1</td>\n",
       "      <td>201601</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1457</th>\n",
       "      <td>2016/1/28</td>\n",
       "      <td>2016</td>\n",
       "      <td>Q1</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>2016Q1</td>\n",
       "      <td>201601</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458</th>\n",
       "      <td>2016/1/29</td>\n",
       "      <td>2016</td>\n",
       "      <td>Q1</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>2016Q1</td>\n",
       "      <td>201601</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1459</th>\n",
       "      <td>2016/1/30</td>\n",
       "      <td>2016</td>\n",
       "      <td>Q1</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>2016Q1</td>\n",
       "      <td>201601</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1460</th>\n",
       "      <td>2016/1/31</td>\n",
       "      <td>2016</td>\n",
       "      <td>Q1</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>2016Q1</td>\n",
       "      <td>201601</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1461 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             日期    年度  季度  月份   日    年度季度    年度月份  星期几\n",
       "0      2013/7/1  2013  Q3   7   1  2013Q3  201307    1\n",
       "1      2013/7/2  2013  Q3   7   2  2013Q3  201307    2\n",
       "2      2013/7/3  2013  Q3   7   3  2013Q3  201307    3\n",
       "3      2013/7/4  2013  Q3   7   4  2013Q3  201307    4\n",
       "4      2013/7/5  2013  Q3   7   5  2013Q3  201307    5\n",
       "...         ...   ...  ..  ..  ..     ...     ...  ...\n",
       "1456  2016/1/27  2016  Q1   1  27  2016Q1  201601    3\n",
       "1457  2016/1/28  2016  Q1   1  28  2016Q1  201601    4\n",
       "1458  2016/1/29  2016  Q1   1  29  2016Q1  201601    5\n",
       "1459  2016/1/30  2016  Q1   1  30  2016Q1  201601    6\n",
       "1460  2016/1/31  2016  Q1   1  31  2016Q1  201601    7\n",
       "\n",
       "[1461 rows x 8 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>产品类别</th>\n",
       "      <th>产品ID</th>\n",
       "      <th>产品型号</th>\n",
       "      <th>产品名称</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>配件</td>\n",
       "      <td>528</td>\n",
       "      <td>Rawlings Heart of THE Hide-11.5</td>\n",
       "      <td>棒球手套</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>配件</td>\n",
       "      <td>480</td>\n",
       "      <td>Rawlings Gold Glove-11.5</td>\n",
       "      <td>棒球手套</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>配件</td>\n",
       "      <td>537</td>\n",
       "      <td>Mizuno MVP-12</td>\n",
       "      <td>棒球手套</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>配件</td>\n",
       "      <td>529</td>\n",
       "      <td>Wilson-A2000-12.5</td>\n",
       "      <td>棒球手套</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>配件</td>\n",
       "      <td>536</td>\n",
       "      <td>Rawlings Pro Preffered-13-FirstBaseMitt</td>\n",
       "      <td>棒球手套</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>配件</td>\n",
       "      <td>214</td>\n",
       "      <td>Easton-Z5</td>\n",
       "      <td>头盔</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>配件</td>\n",
       "      <td>478</td>\n",
       "      <td>Marucci CAT Composite</td>\n",
       "      <td>球棒与球棒袋</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>配件</td>\n",
       "      <td>479</td>\n",
       "      <td>Marucci CAT9</td>\n",
       "      <td>球棒与球棒袋</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>配件</td>\n",
       "      <td>477</td>\n",
       "      <td>Bat Pack</td>\n",
       "      <td>球棒与球棒袋</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>球</td>\n",
       "      <td>322</td>\n",
       "      <td>BA-150</td>\n",
       "      <td>硬式棒球</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>158 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    产品类别  产品ID                                     产品型号    产品名称\n",
       "0     配件   528          Rawlings Heart of THE Hide-11.5    棒球手套\n",
       "1     配件   480                 Rawlings Gold Glove-11.5    棒球手套\n",
       "2     配件   537                            Mizuno MVP-12    棒球手套\n",
       "3     配件   529                        Wilson-A2000-12.5    棒球手套\n",
       "4     配件   536  Rawlings Pro Preffered-13-FirstBaseMitt    棒球手套\n",
       "..   ...   ...                                      ...     ...\n",
       "153   配件   214                                Easton-Z5      头盔\n",
       "154   配件   478                    Marucci CAT Composite  球棒与球棒袋\n",
       "155   配件   479                             Marucci CAT9  球棒与球棒袋\n",
       "156   配件   477                                 Bat Pack  球棒与球棒袋\n",
       "157    球   322                                   BA-150    硬式棒球\n",
       "\n",
       "[158 rows x 4 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "product"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 除了order表其他三个表没有用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 60398 entries, 0 to 60397\n",
      "Data columns (total 17 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   订单日期    60398 non-null  object \n",
      " 1   年份      60398 non-null  int64  \n",
      " 2   订单数量    60398 non-null  int64  \n",
      " 3   产品ID    60398 non-null  int64  \n",
      " 4   客户ID    60398 non-null  object \n",
      " 5   交易类型    60398 non-null  int64  \n",
      " 6   销售区域ID  60398 non-null  int64  \n",
      " 7   销售大区    60398 non-null  object \n",
      " 8   国家      60398 non-null  object \n",
      " 9   区域      60398 non-null  object \n",
      " 10  产品类别    60398 non-null  object \n",
      " 11  产品型号名称  60398 non-null  object \n",
      " 12  产品名称    60398 non-null  object \n",
      " 13  产品成本    60398 non-null  float64\n",
      " 14  利润      60398 non-null  float64\n",
      " 15  单价      60398 non-null  float64\n",
      " 16  销售金额    60398 non-null  float64\n",
      "dtypes: float64(4), int64(5), object(8)\n",
      "memory usage: 7.8+ MB\n"
     ]
    }
   ],
   "source": [
    "order.info() # 没有缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order.duplicated().sum()  # 有7条重复数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "order = order.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>年份</th>\n",
       "      <th>订单数量</th>\n",
       "      <th>产品ID</th>\n",
       "      <th>交易类型</th>\n",
       "      <th>销售区域ID</th>\n",
       "      <th>产品成本</th>\n",
       "      <th>利润</th>\n",
       "      <th>单价</th>\n",
       "      <th>销售金额</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>60391.000000</td>\n",
       "      <td>60391.0</td>\n",
       "      <td>60391.000000</td>\n",
       "      <td>60391.000000</td>\n",
       "      <td>60391.000000</td>\n",
       "      <td>60391.000000</td>\n",
       "      <td>60391.000000</td>\n",
       "      <td>60391.000000</td>\n",
       "      <td>60391.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2015.456359</td>\n",
       "      <td>1.0</td>\n",
       "      <td>437.552897</td>\n",
       "      <td>1.438476</td>\n",
       "      <td>6.244374</td>\n",
       "      <td>370.168304</td>\n",
       "      <td>271.375418</td>\n",
       "      <td>641.543722</td>\n",
       "      <td>641.543722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.661364</td>\n",
       "      <td>0.0</td>\n",
       "      <td>118.088624</td>\n",
       "      <td>0.736586</td>\n",
       "      <td>2.961271</td>\n",
       "      <td>508.012963</td>\n",
       "      <td>398.134438</td>\n",
       "      <td>867.702552</td>\n",
       "      <td>867.702552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2013.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>214.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>7.800000</td>\n",
       "      <td>7.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2015.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>359.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>59.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2016.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>479.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>59.900000</td>\n",
       "      <td>219.900000</td>\n",
       "      <td>219.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2016.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>529.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>349.000000</td>\n",
       "      <td>1099.000000</td>\n",
       "      <td>1099.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2016.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>606.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1999.000000</td>\n",
       "      <td>1200.000000</td>\n",
       "      <td>3199.000000</td>\n",
       "      <td>3199.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 年份     订单数量          产品ID          交易类型        销售区域ID  \\\n",
       "count  60391.000000  60391.0  60391.000000  60391.000000  60391.000000   \n",
       "mean    2015.456359      1.0    437.552897      1.438476      6.244374   \n",
       "std        0.661364      0.0    118.088624      0.736586      2.961271   \n",
       "min     2013.000000      1.0    214.000000      1.000000      1.000000   \n",
       "25%     2015.000000      1.0    359.000000      1.000000      4.000000   \n",
       "50%     2016.000000      1.0    479.000000      1.000000      7.000000   \n",
       "75%     2016.000000      1.0    529.000000      2.000000      9.000000   \n",
       "max     2016.000000      1.0    606.000000      3.000000     10.000000   \n",
       "\n",
       "               产品成本            利润            单价          销售金额  \n",
       "count  60391.000000  60391.000000  60391.000000  60391.000000  \n",
       "mean     370.168304    271.375418    641.543722    641.543722  \n",
       "std      508.012963    398.134438    867.702552    867.702552  \n",
       "min        4.000000      3.800000      7.800000      7.800000  \n",
       "25%       29.000000     28.000000     59.000000     59.000000  \n",
       "50%      160.000000     59.900000    219.900000    219.900000  \n",
       "75%      500.000000    349.000000   1099.000000   1099.000000  \n",
       "max     1999.000000   1200.000000   3199.000000   3199.000000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 订单时间是2013年到2016年\n",
    "2. 订单数量都是1\n",
    "3. 交易类型是1、2、3\n",
    "4. 销售区域是1、2、3、4、5、6、7、8、9、10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>订单日期</th>\n",
       "      <th>客户ID</th>\n",
       "      <th>销售大区</th>\n",
       "      <th>国家</th>\n",
       "      <th>区域</th>\n",
       "      <th>产品类别</th>\n",
       "      <th>产品型号名称</th>\n",
       "      <th>产品名称</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>60391</td>\n",
       "      <td>60391</td>\n",
       "      <td>60391</td>\n",
       "      <td>60391</td>\n",
       "      <td>60391</td>\n",
       "      <td>60391</td>\n",
       "      <td>60391</td>\n",
       "      <td>60391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>1124</td>\n",
       "      <td>18484</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>40</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>2016/6/14</td>\n",
       "      <td>13206BA</td>\n",
       "      <td>中国澳门</td>\n",
       "      <td>中国</td>\n",
       "      <td>大中华区</td>\n",
       "      <td>配件</td>\n",
       "      <td>Easton-Z5</td>\n",
       "      <td>棒球手套</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>263</td>\n",
       "      <td>68</td>\n",
       "      <td>13345</td>\n",
       "      <td>21344</td>\n",
       "      <td>47218</td>\n",
       "      <td>36085</td>\n",
       "      <td>6439</td>\n",
       "      <td>17327</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             订单日期     客户ID   销售大区     国家     区域   产品类别     产品型号名称   产品名称\n",
       "count       60391    60391  60391  60391  60391  60391      60391  60391\n",
       "unique       1124    18484     10      6      3      3         40     17\n",
       "top     2016/6/14  13206BA   中国澳门     中国   大中华区     配件  Easton-Z5   棒球手套\n",
       "freq          263       68  13345  21344  47218  36085       6439  17327"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order.describe(include='object')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 销售大区有10个\n",
    "2. 国家有3个\n",
    "3. 区域有三个\n",
    "4. 产品类型三种\n",
    "5. 产品型号40种\n",
    "6. 产品名称17种"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 类别数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 销售大区"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "中国澳门    13345\n",
       "西南区     12265\n",
       "西北区      8993\n",
       "韩国       7616\n",
       "中国台湾     6905\n",
       "中国香港     5624\n",
       "新加坡      5557\n",
       "东南区        39\n",
       "东北区        27\n",
       "中部         20\n",
       "Name: 销售大区, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order['销售大区'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号\n",
    "fit, ax = plt.subplots(figsize=(10,5))\n",
    "sns.countplot(x='销售大区', data=order, ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 国家"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "中国      21344\n",
       "中国澳门    13345\n",
       "韩国       7616\n",
       "中国台湾     6905\n",
       "中国香港     5624\n",
       "新加坡      5557\n",
       "Name: 国家, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order['国家'].value_counts()  # order是在中国、韩国和新加坡的订单数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1597: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.obj[key] = value\n",
      "D:\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1676: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._setitem_single_column(ilocs[0], value, pi)\n"
     ]
    }
   ],
   "source": [
    "order.loc[:,'country'] = order['国家'].apply(lambda x: x[:2] if len(x)!=3 else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "中国     47218\n",
       "韩国      7616\n",
       "新加坡     5557\n",
       "Name: country, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order['country'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "78.19%的订单来自中国\n"
     ]
    }
   ],
   "source": [
    "print('{:.2f}%的订单来自中国'.format(47218/order.shape[0]*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 区域"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "大中华区    47218\n",
       "韩国       7616\n",
       "新加坡      5557\n",
       "Name: 区域, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order['区域'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 产品类别"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "配件    36085\n",
       "球     15205\n",
       "服装     9101\n",
       "Name: 产品类别, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order['产品类别'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "都是配件、球、和服装订单"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 产品型号名称"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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li3KLAJcBC/RQ02sAd07j497MzMzMzMzM7HPPKYNsmhYRL5AmeAc3uSufJzsC75AmjIFJ3/OTpDvBpxU75u2kQFFEfAbcCgwAvtKMTvWy3wMPAxt3Ue7nwHhg1e42KGlO4GvA8O7WZWZmZmZmZmZmvcsBAZumSZoeWJCUrqS4/7uS7pT0Ts6Df6OklQrHH5I0Mr9fKOc7H5E/L5I/H9GNfnW4hkBlHQJJC0q6XNK7ksZIOlXSjKWyu0h6VNIHebuHpCskfSiprXS9d0l6O19ze75Dvt5+z0VKNfNIlbvntwA2LJVfTNLFud3xkm6VtEa97RbqW0PS3fn6Rkvao9G6gNWAz4BRpf1HAysBLzbQv9klHS/ppbwmwShJu5bKnJO/izlK+7fJ42LTwr5ZJR0j6bl8zY9L+nVlHEiaXtKgLl7zFJo5JCK2Bd7v4lLOBNaPiJ7I8f9N0n9Ltpb0al5z4kFJe0pSD9RvZmZmZmZmZmY9xAEBm9bMJGlAfi0HDCWlPTm/UkDS+sC1wKzAIaS7pr8M3CBp7lzsPmDpPGG5Qt63rKTpSOlyIK1X0FvmJt1R/RnwS+Ap4KfAfoXr2BT4Gym/+/7ASNLd7u8CPyKl8EHSKqTrnQX4LemaZyVd7yJ19mtw3k6aKK5836SnBl4p7F8C+A/wPeBU4DDSugPtamCh5xywuQ1YHDgS+CvwZ+Dr9daVDQbejIhPcv1z5euYCLwMfFJn/2YhPV1wEPAv0l32Y4Ghko4pFD2f9P1vXqriB8Ab+VwkzZDf/wy4lPTb3wEcS1rzAGAR4NUuXldUGoiIJ2q5loh4KiLqWty5E5XFpucCTiaNvwmka/hVD7VhZmZmZmZmZmY9wGsI2LRmS6bMYT8BOAk4qrBvWeAaYJ+IGAMg6WngOlKu8+tJk/17kRYmXj4f24CURmZZIEhBg96yAjAkIvbO/buMNAn/rXw9AN/J2x0i4n3gbEmbAPOVcrWvA0wP7BERD+f6biBNxg4AXqqjX1/I2w9zPTOQJrGLKnd9nwJ8EVglIh7M5c8F/gv8VdLgymR8jQ4FZga+V6hvJGnS/K066iley6uFz9cBaxY+rwe011HfT0l3w/8sIk7L/fs/0ng6RNLFEfEoKdDzIikAcEEuNyewEfDXiJiY69sBWJe0APUNed8wYCCwi6RfkcbEBl306+06rqE3/IcUJDml8lSJpNOAm4GDJJ0cER+WT5K0F+lvkIEDB/LwvPP2YZfNOjdhhhk8Jq2leExaq2nmmJyhvb0p7VprGzduHO0eG9ZCPCat1XhMWpEDAjataQeOIU10XwT8KyJ+XiyQJ2tPy3e2bwysTJp8BZgvbyuT/cuSAgL3k+7GXj7vezoienOi9SPgN4U+vynpVSZPyEO6W/wnwB45YLAmaQL+6VJdd5MCGMdKOgN4MCKeB/ZpoF+VNEEzFD5XJqQPJqcMynfLbwgMr0ze5+t4O6dK+jWwCnBPHW2vDTxRqu8GSS83cB2Vvhf/jTuA9GTGhqRrqddmwMekdDuV/oWkk4Hv5uOP5n0XAAdLmjci3iQFsWah8CRLPofSvqLlIuI24JYG+tpnIuLaKvs+lXQW6cmHb5DGaLnMEPKTEEsuvnis8Oabvd1Vs5o9PO+8eExaK/GYtFbTzDE5cNttm9Kutbb29nba2tqa3Q2zSTwmrdV4TFqRUwbZtGZMRNwSEZcAlwNb5tRBk0haWtJw4HXgH6SUNv8p1fM4KfVOJSAwKr8qAYHeTBcE8EyeKC76rPT5VtId7ieQ7vK/JO87vFgoIu4lLaA7gHSH+f9yTvqDGujX2LydL9cd+fu+hSnv0p+XNNle7emDSm7+QXW2PQ9T3tFf8b8666kYC8yb00AREffn63i2wfoGAa8W7vCvqHa95wMzAtvkzz8gBTtGFMoMJP3mG3TwKq99MK0Zl7f1pq0yMzMzMzMzM7Ne4oCATcsqaYIOK+0fBixJSqUzZ0SsRkpvM0nOnz6ClJ9+KSYHBFYAlqF30wXB5In3zpxDeiJiELAW8OWI2CAi3i0XjIiLI2JVYHbSNQ0HTpC0U539eg54D1ixMpFesFDh/VukXPwLV6mjsm9MnW2/S5okL5u/znoqHgJmApYr7V9o6qI1GQMMUlrIumiq6825/O8HfiBpICkVVPlJgLGkf4NHVYIuOWDxEJMn01uapFkkXSDp+1UOVxbxfr0v+2RmZmZmZmZmZh1zQMCmWRHxCHAlsI2kZQAkzUua4L81Iu7KqUsE7FmlinuBrUnpe54lBQQ2JqXt6e0nBGqxIjA+IsZGxN05DdBUJA2V9JSkGSLi45xy58B8eNV6GoyIz0hPVcwL7Fpo40vA6oVyE0ipbNaVtEKh3FzAbqTc98W74Wvxb9LCzssU6mtj8kLH9bo4byc9KZEn87epXrxL15DWONi7UJ9ICz5HPl70d1IapJ+T1ni4oHS8sm7ArqX9R5G+iwUa7GefyWsDfB34naTZKvslLUZK0TSW+tJGmZmZmZmZmZlZL/IaAjatO4qUn/1Q0uK7b+ac85tLOpiU83170joCALMWzr2PFBSr5H0flT9PoDXStdwB7CppAvAI6c79d4HHIuLFQrnhpEnlGyVdAXxCumaA2xto91DSgsZnSVqKlMbnJ0z978UBpMnedkmn5P79mJRuaKuI+LjOdo/J7d4s6VTSb/FL8gLHDbgI2Im0QO/MpO9iG+CrDdZ3aj7/NEmVNFNbA+sDx0dEecxcAvyZtJ5Ce+k3A7gQ+CFwXA6C/Jv0hMrewKU54NVUkjYA5i8tYl32O+CfwEhJN5ICalvn7Q7VFhQ2MzMzMzMzM7Pm8BMCNk2LiIeBq4Dt8+Q1wCakNQN+R8q3/xJp0vbTfKyi8hTAqFzXq6Q7mkdWyRPfDOeS7jzfkpTy6GLgeuAFSadXCkXEeaQ1BGYhBUhOIi0+vGtEXFlvoxExhvQ0wOXAvsCRpODJaaVyT5KeQLiJFBw4GngZWC8iynfL19Luv0m581/K1/GTXGdD6ZtyWqgtgGNJ6aNOJgU19m+wvgnAeqTvd9Nc33zAHhHxqyrl32DyUwBTLRycx9h3c//aSN/vBqQUWOWnBprlt3S86DEAEXEZaUHlcaSFrDcDbgPWiIh/9noPzczMzMzMzMysZkpzZmbWSnJw47+kIMBw0l3y05FS+ewNzB0RjebWN2spSy6+eNx98MHN7obZJA/POy8rvFle992seTwmrdU0c0wO3GefprRrra29vZ22trZmd8NsEo9JazUek/2TpJERsXJ5v1MGmbWmZ0h3yW9FuuP6C6SgwGukND0nNq9rZj1LM8zg/7m3ljJDezsDt9222d0wm8Rj0lqNx6SZmZnZtMsBAbMWFBGfklL1HNnsvpiZmZmZmZmZmdnng9cQMDMzMzMzMzMzMzPrBxwQMDMzMzMzMzMzMzPrB5wyyMzMmiomfsJrZx7d7G6YTTJxwOL9fkwO+vGhze6CmZmZmZmZ9QI/IWBmZmZmZmZmZmZm1g84IGBmZmZmZmZmZmZm1g84IGBmvUZSm6TIry0K+7co7G+rct5oSe1919O+l68xJA0r7X877x8q6ev5/T+rnD+TpHGSXsifjyh8pyHpU0n/k3S6pLm62dfFJL0mabcOjn9J0iWSxkqaKGlM7o//G2NmZmZmZmZm1kK8hoCZ9ZUVgWH5/UpdlD0A+KAX+9JKVqy8kbQYMHflc0Q8IOl/wLckTRcRnxXOWxOYHRhaqu9kYCQwB/BNYG9gVUlrRMTEejsnaUHgBmD+Do7PC9wODACGAM8COwCHA7MAv663TTMzMzMzMzMz6x2+e9PM+sqKHbyfSkQMi4ibe7U3rWNRSV/M71escvxa4IvAKqX9G+btNaX9wyPigog4MyJ2A47O525Tb8ckrQM8AMzaSbFDSMGA1SLi4Ig4C/g28BTwY0kOPJuZmZmZmZmZtQgHBMysr6zUwXubHAio9r1UJvw3LO3fEBgHtHdR97/ydvUG+nUycBmwRydlHgR+GBGPV3ZExMfAI8CcdB5MMDMzMzMzMzOzPuSAgJn1hduBL0n6oqR5gEWA2zoq3NkaApJ2K+XKL76OKJQbKimqnH9ycX+lXM7J/ydJr0p6T9K1OV1O8dyBkv6Sc+R/KOk+SRvX/W1Mdg/wEZMDAisy9fdyKzCeQkBA0gBS8OCmiPioizbG5+3M+RoHdfEqrjewQ0TsB3zSUeURcWFEXF7cJ0nAqsBzEfF+F/0zMzMzMzMzM7M+4lQOZtYXRgDrkSaxlffdD6zfQF13ADuX9u1LugP+0UY7CFwIfBk4ipR7f1fgTGBzmDQJ/29gYeB04EVgN+AaSftExJAG2vwEeJjJTwasBFxE4XuJiA8l3QJsJGmOPMG+Ael7LKcLqubrefsEsAYpONOZv5Gui4h4orbLmMqOpKDPLzsqIGkvYC+AgQMH8OiAxRtsyqznTZhh5n4/Jp9ob292F6xg3LhxtPs3sRbiMWmtxmPSWo3HpLUaj0krckDAzPrCk8D7pDvgld8/2UhFEfEc8Fzls6QdScGA4yLism708SvA6hExAThT0vLAtwrHjwQWBzaLiGty22eRAhsnSboyIt5ooN0RwDp5cd6F8+eya4DNSIGCq0hPC3wGXFel7Bw5ePEFUmDjOOBtUsBjIimY0JlXGriGSSQtAPyZtIbAqR2VywGUIQBLLv7lWG7ss91p1qxHPTpgcfr7mBy07Q+a3QUraG9vp62trdndMJvEY9JajcektRqPSWs1HpNW5ICAmfWFz4CRTH5C4IG8r1skrQWcA1xJWty2Ow7NwYCKR5kyp/9mpBQ4k+7Kj4iPJZ0BnEWaaL+ogXZHAHsDq+XP91cpcx0QpEDAVbmt+zoIQPy99PlpYMtC2Vsa6GNN8gLClwBzAN+pIZ2RmZmZmZmZmZn1IQcEzKyvjAC+RwoI/KuLsl2StAQwDHgM2CkiplovoANzdrD/3tLncsBiEHB3lfNeLBxvxAjSv8U7AWMjYnRKwT9ZRLwq6X5gQ0nLAgsBZ3RQ3+GktQk+A8YA/63ju+mu04F1gF0i4sE+atPMzMzMzMzMzGrkRYXNrK+MAJYDlqX6XfA1ywsTXwd8CGwaEeO7OKXoa9V2RsTYLs4bQ0rpU7Zw4XgjngDGAd+n8+/lGmAJct59Ol4/YFRE3BIRt0XEY30VDJD0a9KTDn+IiPP7ok0zMzMzMzMzM6uPAwJm1ldGdPC+LpJmAq4g3SW/aUR0lPN+XC6/YOHcDYCVG2z6GmBxSRsV6psR+AkwAbi5kUojopJOCTr/XioBgJ8BoyOiOwso9yhJBwLHklImHdrk7piZmZmZmZmZWQecMsjM+kROhTMWmC4inpO0ToNVnQisC1wMLJtT6FSMi4hh+f3tpMn64yUdAiwPnE1KMVQ8p1aHA98BLpd0OilV0K7ACsC+EfF6A3VWjCBdU4dPCETEQ5JeAhah46cD+pykDUmLCL8F3ArsWEp5dHNENPr0hJmZmZmZmZmZ9SAHBMysL1Xy5XdHJeXPD/Kr6AXSugJExOWSjgD2BJ4EHiFN4G9EAwGBiHhd0mrAMbmeOYFRwOYRcXXdVzGlypMBXaVSuhb4cd62iq3ydh7SAs9l69F4OiUzMzMzMzMzM+tBDgiYWa+JiHbSIsKVzxsV3g8FhnZw3uBO6myro/0jgSNLu28CDiiU2Q3Yrcq5U+3PTwHsmV/dUrzGiPgH8I/CZ3Vwzr7Avh0cOwI4orv96qDudgq/Y+nYPsA+vdGumZmZmZmZmZn1LAcEzMysqTTDjAz6sZcesNbxRHs7g7YtP4BkZmZmZmZmNu3zosJmZmZmZmZmZmZmZv2AAwJmZmZmZmZmZmZmZv2AAwJmZmZmZmZmZmZmZv2A1xAwM7Omiokf8/Lp3V6n2azHfDL/Wh6T1lL665hceL+/NrsLZmZmZmafO35CwMzMzMzMzMzMzMysH3BAwMzMzMzMzMzMzMysH3BAwGwaJ+lpSU/m9/NLCkn35M/L5M9/Kp0TkobWUPdoSe290e++JOkFSe9LmqmD45tJul/SBEkfS3pA0npVyi0r6TpJ70gaI+lISVP9O9rT5Rq43n0lRSfHa7reGttaJI+naq+lG78KMzMzMzMzMzPraV5DwGza9zywRn7/lbxdMm8XytvnSufsDDzby/1qCZJWAL6UP7YBN5WObwVcBjwM/BaYBTgQuFHSyhExKpdbEriDFEg9AZgZ+BXp39HfFurr0XINXO92wGmdHK/peuuwet7+FHindOyVOusyMzMzMzMzM7Ne5ICA2bTvOWADSXMzORAwr6QvAgsXykwSERf0XfeabtPS+0kBgfzEwGnADcBmETEx778HuB3YB9g3Fz8RmBNYNSIezOX+B5wu6eyIeL6XytUkP1nwe1JQ4TVgwSpl6rneWq0BvBERp9d5npmZmZmZmZmZ9TGnDDKb9lUm+xcmPSFwA/ApKTjQ0RMC/cmmwBNAO1MGBwDmBk4FflKZHM/uz9v5ASTNCXwXuLYyeZ+dC0wAtuyNcnX6GrBXPvfmDsrMTQ3XW6c1geENnGdmZmZmZmZmZn3MAQGzaV9lsn8hUhDgceBFUnBgYeAzYHTxhFrXEOiKpO9KukvS2zkPfruktaqUW0HStTmP/3uShklaplSmPa9ZMKekcySNlfSWpAvyBDqSviBpUBevWQt1DgJWAW4j3QG/qKSvVY5HxOsRcVyVu/FXy9uH8varpCeqbisWioiPgVHAN3qjnKS5arjeyroILwHLRMQ15e+/geutiaTZgBWBLyutZfGhpOclHSvpC/XUZWZmZmZmZmZmvc8pg8ymfeUnBG4FnmHyEwIvR8QnPd2opFWAa0mTyL8FAtgNuEHSVyPipVxuDeAWUn75o4Hpgf2B+yStGxEPFKqdkfSEw4Rc58bAjsCb+ZxfAId30bXdgaH5/caASMGAMcCRpKcEHumijl8BHwJ/y5/nydtqT1qMAQb3UrlTgF276Ot6QHtEvNVFuc6Ur7dWq5H+OzJ/PvcV0hMDvwZWIj0FUZWkvUhPNDBw4AAen3+qOJJZ03w4wxc8Jq2l9Ncx+Ux7e7O7YB0YN24c7f59rIV4TFqr8Zi0VuMxaUUOCJhN+yqTyosAS5CCAc8w+QmB3koXtA5pcn+PiHgYQNINpMnlAaQ71gGGAJ8Aq0TE/3K5S4BHgbOAVQt1Lki6Q37jiPhM0rm5nm/l438H7uqiX48V3m9KClS0A++RAg2bAn/o6GRJPwK+DRwZES/m3ZWnqcZVOWU8MFcvlTse6Gq9h4e7ON6pDq63VmNJAZozI+KNvO8MSc8Ah0n6RkSMrHZiRAwhjQ2WXHyx+OqYrn5Ws77z+Pxr4TFpraS/jsmFt+0qJm7N0t7eTltbW7O7YTaJx6S1Go9JazUek1bkgIDZNC4i3pH0DvBNYFYmBwR2JD0h8GDHZ3fL3aTJ9mMlnQE8mFPR7FMpIGlxYFng3EowIPf5OUlXAd+XtGBEvFKo9+CI+CyX+0TSU8CXKudRY4BD0izABsBTedecwEhgDUnzRcTrVc5ZgZRj/27S0wwV4ytFqjQ1HTBLb5SLiP8C/61Spkd0cr01iYhRpABO2enAYaQnBKoGBMzMzMzMzMzMrO95DQGzz4fnSJOvnwAvkAICXwcG0ktPCETEvaSgwwBgGPA/Sc9JOqhQbFDevsTUXiyVARgXEY+Wyn3WYBfXB2YDlgLeyK+1SP/ubVwuLGl+4GrgLWCb0qK7lWDGYlXamQ94t5fK9Zourre7Kk8+LNKDdZqZmZmZmZmZWTc5IGD2+VCZ9B8dEZ+SAgLlYz0uIi6OiFWB2UkBiOHACZJ2ykXG5O3CVU5fuFQG0loBPWXTvP0Z6UmBDYCdSseAtFgxaT2EeYFNI+K1Ul3Pkibp1y2dNz2wMil3fm+U6xU1XG+t9Rwq6Y9VDq2Ut1M9hWFmZmZmZmZmZs3jgIDZ50Nl0v+ZwucoHetRkoZKekrSDBHxcUQ8CByYD68KEBHPAE8A20haoHDuYGAL4IFiKqEetglp3YCzIuKW/LoQGA1sIGnm3JcvAP8CVgS2y9cxhRxkGQZsLelLhUM/IOX7v7E3yvWGWq63DgsCB0haulD/bMCx+ePV3ajbzMzMzMzMzMx6mNcQMPt8mCIgEBEfSnqZlLKlt54QGA7sCtwo6QpSuqLt87HbC+X2Am4CRkg6jRSI3J8UsNiHXiBpJdITCJdFxCelw9cBPwHWA24A/kJKJfQvYJ7C0w2QUhgNy+//AGwD3CDpt6RUR8cDrwIXFc7p6XI9rabrlbQ8sDxwc0SMmaqW5E+ktFF3SboM+JiUjunLwKkRcX/vXIKZmZmZmZmZmTXCAQGzz4fyEwKV9/NUWzy3J0TEeZI+Ik2uH0VaCPdJYNeIuLJQ7k5JawDHkBaaDVLA4DdV1gvoKZWUQNdXOVYJCGxKCghsnfd/L7+KXiDdyU9EPCVpI+BvwBX5+LPAjhHxVuWEni7XC2q6XmAr4HBS4KRqQCAinpe0JumJgB3y7odIv+2lPddlMzMzMzMzMzPrCYqIrkuZmRkAkmYE1iA96XBPRHzUF+U+z5Zaaql48sknm90Ns0na29tpa2trdjfMJvGYtFbjMWmtxmPSWo3HpLUaj8n+SdLIiFi5vN9PCJiZ1SGnIBre1+XMzMzMzMzMzMy6y4sKm5mZmZmZmZmZmZn1Aw4ImJmZmZmZmZmZmZn1A04ZZGZmTRUTP+KJMzZvdjfMJvlwvs144oyTer2dpX9yVa+3YWZmZmZmZlbkJwTMzMzMzMzMzMzMzPoBPyFgZp2SNJj0b8XzEfFpk7tjZmZmZmZmZmZmDeqVJwQkDZYUpddrkm6WtFFvtNlBP0ZLaq+hXEga2vs96nlVvufxku7uy++5M63QP0kHSXpa0seS3pE0pDvlmq3w93VEN+s5XtLoTo5vKukl4HngaeB1Sfs20E61fw/Kr1k6OOeIetvrLZLaqvT7XUk3SFqpBfq3W6FfKxb2H1DYP7jKedPsv39mZmZmZmZmZlaf3n5C4F7gDGBGYDFgT+A6SXtGxNm93HY9dgaebXYnuuFF4LekAM/CwG7AtZK2jogrm9mxrGn9k7QzcAJwEfAHYA5goUbLfV5IOgg4GHihg+PfBK4ALgdOIv12RwBnSHomIm5qoNk7gY6CLB+XPr9B+rsc1UA7DcsBiIciYlgnxa4kfTeQxvPPgNslLRMRr/RuD2u2IvBQft9VsKLLf/8kHQC8ExFDe6KcmZmZmZmZmZk1R28HBF6IiAsqHySdAzwB/AJomYBAsY/TqLdL3/NZwFPAsaTJy2ZrZv/WytvdI6I86dxIuWlavhP//4AdSJPuHfkTaUJ5h4j4LJ+7PfAmsDXQSEDguVr/1iLiA6AZf5eHA38DhnVSZlRpPN9FCnb8nPRvWytYsYP3U6nxNzkAGA0M7aFyZmZmZmZmZmbWBH26qHBEvAA8Bgzuy3b7m4h4C/gPsJSkLza7P2V93L+Zc5tdTfLXWm5a9x1gfaAN+G8n5Q4DdqsEA7JP8vazKuX7swfy9qtN7cWUVgKQNDOt1S8zMzMzMzMzM2uiPg0ISJoeWBB4rbT/u5LuzHnbx0q6sZiTW9JDkkbm9wvlnNcj8udFuptrvLMc2pV1CCQtKOnynDN8jKRTJc1YKruLpEclfZC3e0i6QtKHktpK13uXpLfzNbdLWqvcdjeNz9uZc5uTcrJLmlXSkZKekHRplWteN/dpvKQ3JZ0vaeEW6t+mSusQjJf0qqRLJS1aOD4p1zuwa95XzPs+uJ5ytbbb09eRy0jSryS9kMfRcGCpUpl5JA3q4jV9Lj4SWC4i7u3sx4mI4RHxWGl3Je3TVbnduWpod6bO2umIulhDoM7veUdJIyW9r/Tvy7WSliscH1oYBwC7FsZAew3dXSJvy/+uVdodp7SGyuWSvlxv/wrluhwv2e3ACpIELEtK2XZbR51XB//+5e+28r0sCqxb+F5G11vOzMzMzMzMzMyar7dTBs0kaUB+Pwj4FbAAcHSlgKT1gWtJ6UkOAWYC9gNukLRURLwD3AfslCe4VsinLitpOtKEF6T1CnrL3MDw3MdfAjsBPyUttnpSvo5NSalGrgVOBtYmpUUaCvwIeCaXW4XJ1/tbIEg59W+Q9NWIeKm7nc2TvysAbwOvlw7PBtwFfAn4F+m7LZ67DXBJ7u9vgXmB/YFvSVotIl5scv/2Av5CmpTeHxhAyuH+H0nLRcQbwOOkvOgAe5F+i50L1VRS5dRartZ2e/o6AP5IGnO3AMcDawL/KLV1BbAunVsMGB0RL3dRbiqSjgE2AL4O/DIibsiHTiEHUjqxHimFDMDMhX8PKj6IiAn19qmgq+95S1LqoVuBg4BZgR8DN0taPCLGk36HW/Ip5zPlWgdjqrVZuI7FgdOAj0ipmCrt7kr6278FOJD0b8hBpLUzlo+IiXX0r97xN4L0vS9GelLgM9JTDOt3/DVWdQX53y3Sv3NjgWPy53ENlJtCvqa9AAYOHMDo+Tars3tmvefjGebqkzH5Wnt7r7dhnw/jxo2j3ePFWojHpLUaj0lrNR6T1mo8Jq2otwMCW+ZXxQTShNFRhX3LAtcA+0TEGABJTwPXAWsA15Mm+/ciTXAtn49tAHwlnx+UJgJ72ArAkIjYO/fvMtLdwN/K1wMpFQuknOvvA2dL2gSYr5Sjex1gemCPiHg413cDKVgyAGgkIDB9nqCcjrQQ7gGku5aPLqV8AfgJafJxzYj4sHhA0qzAmcDLwMoRMS7vv5406XoCsF0T+zcHKbf9daQgS8WzwKXALsCJeRxdkM/5NrB2tTzptZartd2evg5J85K+qweB70bEp6RFfS8Bti+cdxDQVeql17o43pmlSX97nwJfKOw/nq7z/D8MzJXffz+/ig4hBT0a1eH3nFUmwbeOiHcBJN1N+t4HAC9GxL+Bf+dj59P1WgcH51fFx8B2ETGysG8JUuBml4j4KNf9AWmR9SWZnK6py/41MP5G5O2K+fU48EEn11NVRIwiL+os6WhgTAd/RzWVq3LeEHLgZcnFB8fg16+ut4tmvWb0fJvRF2Ny6W2v6vU27POhvb2dtra2ZnfDbBKPSWs1HpPWajwmrdV4TFpRbwcE2kl3ig4ALgL+FRE/LxaIiNOA0yQNkLQxsDJpwVOA+fK2Mtm/LCkgcD+wSH6/LPB0RLzdi9fxEfCbQp/flPQqU06O3kGanNwjBwzWJE3SPl2q625SAONYSWcAD0bE88A+3ejfcky5QOxE0l3LR1Yp+z6wUweTp2uQfqtTK8EAgIi4R9J9wCaSps8T083o3+rAnMDGVF8Qd4Uq+3pCI+32xHV8k/TEzKWl7/xsCgGB0kR0j4uIrZUWI/4j8DtJb0TE6RHxXzpfhwBIqYXy25tIE9tF5b+PenX2PUMKZO1H+jfmfOChiLif9G9Io84H/k4K7C1GuqP/Ykk7R8TlABFxGExKcbYqsCqTnz6Zj8nfWy39q3f8jSU9lbFSfnXnWs3MzMzMzMzM7HOktwMCYyLiFgBJWwNb5/QWj1YKSFqalApjbdITBI+QFpxdslDP48C7TA4IXEZK1VEJCPRmuiCAZyLizdK+8p3ttwKvku6irzw1cAtweLFQRNwraUdSGpFhwAySngfOiIjyXea1epbJAYUJwH87CZD8PSLe6+DYoLyt9pTCi8BqpLu932pS/wbm7R9J33dZOf1QT2mk3Z64jnny9tXS8brT/nRXRHwo6UBgx/w6vYFqXq38e9CDOvueiYhLJc0H7Enq93SSHgOOi4jzG2zzueJ1SLoAeAE4R9KVEfGZpNVJKYRWBN4jpQj7D1M+MVVr/xoZfyNIKZ6WBy4u1GFmZmZmZmZmZv1YbwcEio4CtgYOY8p0J8NIk8zrAP+OiE8lrUzK0w9ARFQWEf46aUHVUaSAwNrAMqTc/b1pbA1lziE9EfEzUh9fyXf+TyUiLibdUTwTKaDxM+AESTWl2qhiXB0TreM7OVbJl15tAeGFgQ9JgZl69VT/Kr/D2+X6JH0T+KSBvtWikXZ74joq33V5Mnf+LnvcDZLmJKWwuqa4+HD+O3yL9NRCq+jsewameAppVtIE/W+Bv0saHRF3drcDETEuB/W+AcwnaRwpDdqY3N6o/N1tQykgUGP/Ghl/I0gpnSA9IfC97l2lmZmZmZmZmZl9HkzXVw1FxCPAlcA2kpYByDnSlwJujYi7cjBApLtly+4lBRQ+It1xPoqUQuML9P4TArVYERgfEWMj4u6OggGShkp6StIMEfFxRDxIeloAUlqRZrqHdPf/HpJmr+yUtBrp6YDrG0gX1JPuIaWI+YGkGSs7c0qWe5kyv3ort1trfSNIefu3yX8XFbvXfQX1eZ+U3uZUSTMX+rcSad2Obk+i9xVJt0gaDhARE/J6AYfmw6tUOeVNJq95UGsbg0jrLIzP5y9FWoz7yoh4OAcDZqTK71Zj/xoZf5V1BD4hPZ3QXWOp7XuptZyZmZmZmZmZmTVBXz4hAOkpgS1JE1475Fz8LwObSzqYtDjn9qR1BABmLZx7HymA8WieYBuVP08gL2jZZHcAu0qqpD16j3SH92MR8WKh3HBgV+BGSVeQJuwqT0zc3of9nUpEjJe0L2m9hxGShpAmNvcn5S4/qMn9e1/SL0gppu6T9Ddgxty/14FTpoV2a60vIl6TdBZpbYrrJV1DCsxs2zNX1mH/IqcH+mfu31DS3+KBpHRF5XUAWtndpHUPLietYTA9sAcp0HJHlfLXADtIOox0rcuQ8vpfWCizvKSdSL/ZoqRFfWcnLWj9iaRnSYv47iFpDDAL6W++kgat+O9al/1rcPyNJKU1ezQiPpoyntSQa4AjJB1PWv/gy6QnFk5qsJyZmZmZmZmZmTVBnwYEIuJhSVcB20s6MiKeBDYB/gz8jjQ5fjPwS1L6nU2AM/PplacARuW6XpU0FngiIib23VV06Fzg+6SAx49Ik4AASDojIvYDiIjzJH1EmuQ9Kpd7Etg1Iq7s816X5JzmbwBHAMeSAi7XAIeUAhtNERFDJL0G/JrUv3GksfKb3uxfT7dbR32VSd89gfVJd3vvRFpHo9dExOWSNiIF735PCm5dDRwREf/rzbZ72JGk728PUgodkQJ2m+XFe8v2Jz2FtC8pVdPLTB042DK/ghQoe4L093I+QES8I2lT0u96HOnu/mtIv/U1pH/X/lVP/+odfzmI8CQ9t6DwH0j/Vu0ELJCv+/BulDMzMzMzMzMzsyZQRDS7D9M8SUuR7oa9mPQEwIekpxfmBfYG5o6IXs37bmY2rVpqqaXiySefbHY3zCZpb2+nra2t2d0wm8Rj0lqNx6S1Go9JazUek9ZqPCb7J0kjI2Ll8v6+Thn0efUM6W7/rYDNSOsafAi8Rsr/fWLzumZmZmZmZmZmZmZm5oBAj8gL7R6ZX2ZmZmZmZmZmZmZmLWe6ZnfAzMzMzMzMzMzMzMx6n58QMDOzpvps4keM+Mumze6G2STj592cEX9xtr9GrbL3Nc3ugpmZmZmZmXXATwiYmZmZmZmZmZmZmfUDDgiYmZmZmZmZmZmZmfUDDgjYJJLaJEV+bVHYv0Vhf1uV80ZLau+7nvY9Se2F76Daa59m97EWko7I/R1cxzlDJUUvdqshkuaQdLykZyV9JOlpSb/ow/br/i77kqR5JD0u6YgOjs8r6S+SXpX0qaS3JZ0madYG2zung7+NS7p1IWZmZmZmZmZm1mO8hoB1ZEVgWH6/UhdlDwA+6MW+tJKdO9h/X5/2om/9Bbil2Z0okjQTMBz4CnAq8DLwfeBPkj6OiFP7oBtXAM8Ab/RBW3WRNBdwNbB0B8dnBq4j/Z2fC4wCvgfsBwwkfZf1Wh24PddXNLqBuszMzMzMzMzMrBc4IGAdWbGD91OJiGG92ZFWEhEXNLsPfS0i/g38u9n9KNmSFKjaLyLOAJB0NvA4cCgpSNCrImIUaSK9pUhajhSsmKWTYnsCKwPrR8Qd+by/ALcB20k6ICJeq6PNeUjBh9P749+ImZmZmZmZmdm0wimDrCMrdfDerBUMzttXKjsi4hPgaOAiSbM1o1Mt4vfAw8DGnZQZDexVCQYAREQAIwGRnhKox+r5vOF1nmdmZmZmZmZmZn3IAQGr5nbgS5K+mO/8XYR053BVna0hIGm3TvLuH1EoVzVPvaSTi/sr5STNJOlPOf/5e5KulbRg6dyBOUf6GEkfSrpPUmeTpD0irzcwOr/fVNLtOT/7XKVyS0u6TNJrkt6X9ICkbUtl6rneBSSdK+l1SeNyfbt23E39StILkj6QdIekjtLLdLqGgKSFJJ0n6SVJEyQ9letWqdw8kk6X9LKk8ZIelvSjcrka3Z+3h0n6cmVnRAyNiAMiYnyh3VklHSPpuTwOHpf0a0kzlvo3aU0ASctJujT/Npt1cN2driFQR7tz5XH+fP7+Rks6TtIs+fj0kgZ18ZqnUOUhEbEt8H5HX15EXBsR5dQ+AKsB44GnOjq3A2sAE4ET81h/X9ItqrLmiJmZmZmZmZmZNY9TBlk1I4D1SE8GVCZr7wfWb6CuO5g67/6+pDuKH220g8CFwJeBo4BvArsCZwKbA0gaQEpzszBwOvAisBtwjaR9ImJII43meosmRsQ7HZT9FXAMcBMphcvHhWPzAe1AACcC7wFbA5dKejUi7qrzehcgrWMwb77eZ4GtgKGSFoqIP5TqO56UCuqEXO/+wKXACl1/C1O5HlgM+DPpe/4G8Efgk7wPSXMCdwPzAGcArwIbAn8FFgKOrKfBiLhV0umknPdPSroKODGnN5pE0gzAv3KfKt/LKsCxwFLA7lWqbwP+D3iMlGf/xXr61kC7fwc2Ak4CniCti3AQMCvwM1JA7vkumhye+01EPFFvf3Of1wLWBP4vIj6q8/S1Sf89mQAcDswJ/Ai4RdK6EXF3I30yMzMzMzMzM7Oe5YCAVfMk6e7iFUkBgffzvrpFxHPAc5XPknYkBQOOi4jLutHHrwCrR8QE4ExJywPfKhw/Elgc2Cwirsltn0UKbJwk6cqIaGQx2PI5T1J94dZBpEndr+dc82XLAvcCp0bEbbl/F5ICA5sB5YBALde7CLBhRNyc6zuHNBH9M6AcEFgDWD4i3sxlFwW2lDQgIsZ2dPFlkuYFlgdOjogj8u5zJb0BfFgoegBpInx9JgeCriQFEg6UdHREfFpruwAR8VNJVwO/IgU/tpY0DPhhRLydi+0ArEsKSt2Q9w0jpcTZRdKvIuL1UtWnkdLpdCcXfj3trg9cGxG/rJws6RVg7vzxNWCDLtp7u4vjnZI0O3A28CZpQr9el5LWD/hHoc4hpKDKIcAmVdrcC9gLYODAAbwx7+YNNGvWOyZOP7fHZDe0t7c3uwufO+PGjfP3ai3FY9JajcektRqPSWs1HpNW5ICAVfMZKZd45QmBB/K+bsl3IJ9Dmgg+pJvVHZonxyseZcq1DjYDnqsEAwAi4mNJZwBnkSZYL2qg3fLE7AcdlJsZ+GkHwQAi4nbg9pxWZh3SneSbkb7v+aqc0tX1bgw8UwkG5DY+zel0qqXkObYSDCjUtyXwBaDmgEBEvCnpcWB7SY+RnlJ4vBAcqPhu7sftHVS1KIXAUR3t3wzcLGlZ0lMPWwCLSlotIj7O7QKc30EVyzF1OqwLemBh3HravQv4tqT98/tHI+K0SsGI+BC4pZv96cpfgSWBTesJCFVUFnYu7Xtd0j+BPSRNFxGflY4PAYYAfGXxwTHwzasa67lZL3hj3s3xmGzcKttc03Uhq0t7ezttbW3N7obZJB6T1mo8Jq3VeExaq/GYtCIHBKwjI4DvkSZx/9XdyiQtQbpD+jFgp7yAaS3m7GD/vaXP5YDFIFKKmrIXC8frFhG1Tsy+Dvyjo4M5hc6pwA9Ia3k8RUqvtEYHp3R1vfNR5XrLk7B11FePzUmpkf5Euqt9XL5z/xcR8WouM5C0AHBHaxqU79LvlKQlASLiqbx9DNg4P2WxAym4cWlu9zPgOx1UVS1gc1qVffWqp93dSE9w/Ib0O34k6Sbg4Iho6Mmcekj6NWkcHhYR1/Vw9eOAmUjX9VoP121mZmZmZmZmZnVyQMA6MgI4OL//PSmfeUPygqfXkVLIbFpc8LUGX6u2s4a7mMeQ1g8oW7hwvDdN6CLo8SdgF1Iu+X9U7v6XVF5vAajpet8g5eKfgqTtSYGd30VEMRd+3XeBdyQinga2y+0tQpoEPwWYH/h2ob3lgNuKQQpJC5FSHdW7sPB1pODDwNL+S0gBgcGFdqcDRhVTA+W1IJbooO56xmdHam43IsYAe+Tj85PWAjgFuJaUKqrXSPoBKRhxYUQc3WAdy5DWovhZRDxTOrwSaS2Jd7rTTzMzMzMzMzMz6xnTNbsD1rJGdPC+LpJmIi2ouxApGPBKB0XH5fILFs7dAFi5waavARaXtFGhvhmBn5AWPr25oxP7yBrAKxHxt0IwYEdg9gbruw5YQtK3S/t/Qrr7+51GO9oZSRtJelnSFgAR8VJEnE36flctFL2BlI5o61IVfwXupP7g5CPAAEnlJyrWy9vHCu3C1E8mHEVadHqBOtutVU3tSlo+f3/7QQoORMSlpMDGEjmY1iskbUta0Hg4OSDRoHdIC0QfWqp/U9I6FzfltEdmZmZmZmZmZtZkfkLAqoqI0ZLGAtNFxHM5z30jTiQtrnoxsGzO9V4xLiKG5fe3kyavj5d0CGmh2rNJE7vFc2p1OOlO9cslnU5KFbQrsAKwb5WFZPvaw8COkk4hXeP6pMnyz2jsaYzfkfLWXyXpNGA06cmAtYEjI+K9nuh0FfcBs5EWOv4a8DKwDGlNg+sL5U4Cvg9cKGlt0poF6+Y+HldYBLhWv8/nXp/XhXiRFGTZmZSHv9L2hcAPgePynez/Jo2BvYFLI+KROtutVa3tPk56cuYPkhYAniGtp7AH6emCt3qjc7lPFwKfApcD20pTPKRxT14QHEk7AWOK61MURcQrks4E9stPiDxEWix6M9KTEj/vjWswMzMzMzMzM7P6OSBgnRlB98dIJeXPD/Kr6AXSugJExOWSjgD2BJ4k3QG+K7ARDQQE8oKmq5Fy2+9KWotgFLB5RFxd91X0vJ+RJv93JE2ojyAFME4D1pU0R0S8X2tlEfGqpFVJ6V/2AGYhTTZ/P99x3ivyosJrku4O34uUwud10sLNhxbKvZfL/Z4U+NiTNPm9L/CXBtp9MC9SfSQpkDQTaVHiw4A/V9ISRcRESd/Nfdkhv17I5U5s5Jpr7F9N7UbEJ5LaCuUWAN4mPVXzm97qH2myfsb8vtqaCbszeZHn80lPEXT2VM0BpOvbB1gLeJX0ux7TyVNBZmZmZmZmZmbWx1T72q5mZmY97yuLD46Lfll1uRCzpnhj3s0Z+OZVze7GNGuVva9pdhc+d9rb22lra2t2N8wm8Zi0VuMxaa3GY9Jajcdk/yRpZERMlY7dTwiYmVlTTTfDzJ5AtJbS3t7OKtt4TJqZmZmZmdnnjxcVNjMzMzMzMzMzMzPrBxwQMDMzMzMzMzMzMzPrBxwQMDMzMzMzMzMzMzPrB7yGgJmZNdVnEz+i/a8bN7sbNg1p2/O6ZnfBzMzMzMzMbJrkJwTMzMzMzMzMzMzMzPoBBwTMrCVJmk/SkpJmb3ZfpkWS5sjf34CeKGdmZmZmZmZmZtM+BwSsWyTNJmmCpKdqKDtaUntPlauhnhUlXSfpTUnjJN0uabXu1tuX8ncRhdeHkh6UtHOz+1YhaQZJv5b0rKSPJP1X0u6F44ML/T+gsH/Fwv7dCvuXlnQfMAZ4EnhT0pmSZumg/VXy79vWwfFZJB0r6UVJ4/OYWLQb5eaQdHzhep+W9ItSmaGl322ipJcknSxptk7KlV+7ldvviqS5JF0EvEX6/l6XdLOkLzVSro52z+ngGi5ppD4zMzMzMzMzM+t5XkPAumsDYBbgK5KWjIguAwN9QdKawG3AE8DhwJzAQcDNkpaKiFdK5XcD5o6Ik/u4q7UYD+wNCJgf+D7wd0nzRMQpTe1Z8jdgB+AC4C7ge8C5kuaOiJNKZVcsvF+pXJGkuYGbgDeB7wCvAbsBBwJvA78plV8GuBbo7CmCfwKbABcB/wF+RBoHK0bE+HrKSZoJGA58BTgVeJn0e/xJ0scRcWqp7QOBsaR/a9cAfgYsBGybj/8FuCW/3wrYEvgD8Hjed08n19WR84E1Sd/bg8A6wJ+BS3If6i1Xq9WB24FzS/tHN1CXmZmZmZmZmZn1AgcErLs2Lb0/sVkdKTk0b9si4m0ASfcCtwIHAL8sld8NGAyc3Ce9q88nEXFB5YOkU4FHgKMknRURHzWrY5I2IQUDzoyIffO+IcDduX9/LZ2yYgfvK34OzAOsVgja/Dzf/b81hYCApK2B84B3Ounf1qRJ/j9HxEF538XAM8AvgKPqKUeasF8J2C8izsjlziZN4B9KChIUDYuI0fn9UEnTAXtIWjYiHouIfwP/zvUskeu/OSLaO7qmzkhan/R3uHlEXJ13/1fSksCBkhaIiFdrLVdHu/MASwOnF8eqmZmZmZmZmZm1FqcMsoZJErAxcAPpLuBNOz2hbw0m3Vn/bmHfXcApQEs8xdCoiPiYFNiYE1imyd3ZI2//VNkREQEMA74AlFM0LZPvsocqTwgA/wA2Kj/BAXwCfFbadyZwPOkJkI5sD3xKuuu+0r/XgYuBbRooNzhvXymU+wQ4GriomA6oAw/kbW/9bk8CWxQm+Ss+ydvP6ixXq9VJT7AMr/M8MzMzMzMzMzPrQw4IWHesAgwipea5HVhT0hd7q7Gc83xQF6/KZPP9wNzAsZJmhjSRHhEHRMTZub7dKnnOgXWBRYu5z6u0/01JN0gaK+kdSXflO63L5SatgZDbuFfSSz38dVRS3cxcaDdyTvrpJR0o6WFJU6WckbSCpGslvS/pPUnDcuqdRqwJjImI50v77wMuBSYU9t0OzAgsm4NJK5DGziQR8WhE3FHq7/dIY+2qUhvrRMTRwFS/VcHXgIcj4s3S/vtzP2aus9z9eXuYpC8X+j00j63xdG6JvH2ti3KT5N+zq3E/T+7H/yLiqtL5XyI9AXNfRIypp1wd1gAmAidKejuPrVvUwboOZmZmZmZmZmbWHE4ZZN1ReSLgdtIE5+6k/PEX9VJ7pwC7dlFmPaCdlBJo1bzdJad1OSUixhbK3gFUFuf9LTCAlPN9KnnC/FbSneFHkSY/dweulfS1iHi2yjln5jLXk3LS96Svk+7iLj/tMB1p4nwd4DrSGgrFPq1Byln/Dumu9umB/YH7JK0bEQ9QI0lzAAOZPEk+SUQMJ98tLmlw3v0AKfCyEvAe6QmH+4Gpgir5vF1JaYI2AS6j9CRARDxR7bySeYBHq+wfQ/quvgQ8XWu5iLhV0unAfsCTkq4CTsypf6r5oqRxwEzAt4B9SGPh7hr6XrEIUA64lA0H2oo7JK1FeoJjW+A5YLtqJ9Zargtrk/57MoHJa3b8CLglj6uprlfSXsBeAAMHDmDcF7dsoFnrr9rb23u1/nHjxvV6G2b18Ji0VuMxaa3GY9JajcektRqPSStyQMC6Y1NSSp4HmXzH86b0XkDgeNLCtZ15GCDnSV+RNHH7E1J+9wMl/aqS+z0iniNNgCLpR8CsneQ//xppIv3QiHg0n9MOPEZa/Pb/SuVXARYGloyIF+u4xmokaQCTFxXehRT4uCAi3iqV3Zo0sb1oZe2EkiGktDCrRMT/cuWX5HPOIgVRajVH3k7otNRk40i59ldkciqnEZ2UX4SUWkekSfmZgXrXS5gut1tWuZN/rjrLERE/lXQ18CvSQsBbSxoG/LDKd14OsDwGbBIR9aTkeY20eHdnqv3W8wLLArOR0iHNUaVMPeU6cylp/YB/VHbktSQeAw4hBXWmEBFDSOORryw+OL7w9pUNNGv9Vds21/Vq/e3t7bS1tfVqG2b18Ji0VuMxaa3GY9JajcektRqPSStyQMAaktOLrECaJP8i8CHwAvBdSTNExMSebjMi/gv8t47yE4A/Sfoz6e7nE4HTJc0aESfU2falwKWS5pS0AfANJueWn6/KKbMBO/VAMADS3dZvFLsDXAL8uErZ6YDtqgUDJC1Omvg9txIMgBQYyXe6f1/SglXy93ekMqk91b8jkuYm5dt/oXRoBJOfEHgBGEsHcjqgoyXtCPwt796mo/IdGE8KKJRV0qXNUme5St9uBm6WtCwpULUFKeXUanmNh4qdSE8ZzAZ8E/gZcJukb+U1CroUER+S/s7qklMCXSVpJdJTKjdKWry8CHWt5bpo64wq+16X9E/SIsrT1RkEMTMzMzMzMzOzXuA1BKxRlTt+v02arH4DWJSUt3/tJvUJAEmzS1pO0nwAEfFpRFxCumt/LHCEpOnrrHPBPGn+FnANaRHakZ2cMiIi7m3sCqbyAekO8Q1IqYAWiIgfRES1O9qviYjyJHzFoLyttp7Bi6UytXiLdEf5wCrHtiA9OfLt0v4RwPKkoEBnTwdMEhEXktIgbVHDor1l/wMWq7K/EsR5t55ykpaUtGShb49FxMakp2JWAsp5b+6OiFsi4uqI+C2wN7AccFid19GwiHiQtFjyQpTSCjVSrk6VdEnVgmZmZmZmZmZmZtbHHBCwRlXWD9iOyZPVvygda5YVgUeAg4s7853vdwCzk9YLqMd5pMntzYE5I2Il0roDHelqcdl6TMyTyrdExJ1dLPjaWbuV8xaucmzhUpku5TvhHwMWy08EFC2aty+X9o8gPfGwEVXWHpD0M0k7l/eTAjnT51c9RgLfkDR7af9qeftKneWuo3r+/0vydnAX/RmVt1/polxDJG0i6YgqhypPYsxUT7ka21xG0vWSlqhyeCVSiqp3aq3PzMzMzMzMzMx6jwMCVrc8aboe8HhE/LMyWU3Koz+B5gcEniAt+vs9SZMmNvPd5asCrzNlCh5IE6FzSKqWNgZgDeDBiLiukBJm757tdu+KiGdI3802khao7M+L/m4BPFBMJVSjC0iT9D8r1DcjKUXTO6SnBIoeBirfX7UnBFYBTpX05UJ9swEbAw9HxPt19u9yUgBo0m8laU7gB8DIiHizznKPAAPy4sxF6+XtY130py1veyKVVDXzA4dL2qq0fzvS38S/6yxXi3eADUnrdEwiaVPSQso35bRHZmZmZmZmZmbWZF5DwBqxIWmB1ylWdYyICZJuAzaWtHREPNGMzkXEm5JOJC36erekv5PG+q6kO+H3rpLP/BrSgrxnS7oTWACYIyJ+k48/DKwm6UjSIq+bkSY7AWbt1QvqWXsBNwEjJJ1GCgruT1qXYJ8G6juFlCbncEmDSBPmO5PWKjggIj4sxlgi4mNJo0hrMIwk3UFedCTp6YF/SzqDtFju7qSUM7vW27mIaJd0M3CcpOlIixofDsxDWmy6rnLA74HvAdfn/r1IChbtDNxFysFftIWksaSFer+ey30M/KXea6nRhaTf+OLCor4bkgI+v4uIsXWWQ9JOwJi8bsJUIuIVSWcC+0laBHiIlH5pM1Kg7ec9fI1mZmZmZmZmZtYgBwSsEZUnAMqTn5CCBBvnMk0JCGSHkCZr9wFOIOWAfwzYsNrEZkT8TdIA0kK9u+TypxWK7AicSpo8nx64E1iTtNjr9yT9JiI+7b3L6RkRcWe+u/0YUh77AG4HfhMRjzZQ38d5keVDSd/Rj4CngF0i4vwOThsBzBUR75YfyIiIZyStmfv3U1LA4j5gn4j4T739y75PmoD/U/78MfDbvK5EXeUi4kFJa5ECFz8hpdZ5jvRd/rlKoOmkQl2vAJcCJ0XEQw1eS6dyAOa7wO+ArUjBlP8CO0TExfWWy84HhgNVAwLZAaRFovcB1gJeJX2Xx9SxSLWZmZmZmZmZmfUyRUSz+2Bm1uskLQ0sTkr91OEkda3lrOcstdRS8eSTTza7G2aTtLe309bW1uxumE3iMWmtxmPSWo3HpLUaj0lrNR6T/ZOkkRGxcnm/nxAws34hp7Dq8qmVWsuZmZmZmZmZmZlNa7yosJmZmZmZmZmZmZlZP+CAgJmZmZmZmZmZmZlZP+CUQWZm1lSfTvyI68/ZqNndMJtk4lxbcf05x1c9ttEe1/dxb8zMzMzMzMx6jp8QMDMzMzMzMzMzMzPrBxwQMDMzMzMzMzMzMzPrBxwQsLpIapMU+bVFYf8Whf1tVc4bLam973ra9yQNLXwHIWmipOckHS1ppmb37/NG0r6SnpD0kaTnJf1G0nSF43/Mv8OWpfP+lvevVtq/rqThkj6Q9Iakv0iau4O255H0uKQjuujjzLnOoR0cny3380VJn0p6X9IFkuap8Wso1zdv7verub63JZ0madbuXEcN7R5WGvuV173dqdfMzMzMzMzMzHqW1xCw7lgRGJbfr9RF2QOAD3qxL63kt8CLwBeBtvx5WWDLTs753MuBojbg5Ih4p5t1HQn8Dvgn8GfgW8AxwDzAL3Kx44C9gd8AV+bzBgM7AMMi4t5Cfd8DrgaeAn4JDAIOAr4uac2I+LhQdq5cduku+jgzcCGwDvB8B8UuBDYFLgbuBr4J7AYsLmmNiIguv4wp27uO9Hd5LjAK+B6wHzAQ+H6pfE3XUaPVgUeActL1N3qgbjMzMzMzMzMz6yEOCFh3rNjB+6lExLDe7EiLuT4iHsrvT5N0HrCbpNWKk9D9UBtwODAUeKfRSiQtBhwC3BAR2+XdQyR9EfiZpD9GxNiIeFvSH4E/StowIm4CfgWIFCSo1Dcz8FfgdWCtiHg7738ZOIsUQBia9y0HXAHM0kUfFwEuAxbopMwmwBbAjhFxUd59liQBuwKrAP+p7VsBYE9gZWD9iLgjt/EX4DZgO0kHRMRr9VxHLXJ/Vwf+GhEXdLc+MzMzMzMzMzPrPU4ZZN2xUgfvbUr/ytvVm9qLz4/tgBmBs0v7b8r7Vy7sOxV4BfitpAWA3YG/R8TjhTLfBRYCzq4EA7Ir8/bbhX2/Bx4GNu6ijz8HxgOrdlLmXeDnhWBAxf15O38XbZSNBvaqBAMA8hMGI0lBkIGFsrVeRy2WAeYGhvdAXWZmZmZmZmZm1oscELBG3Q58SdIXc77zRUh3IlfV2RoCknbrIP94FHObV3L0Vzn/5OL+Qi7/mST9KedTf0/StZIWLJ07MOdcHyPpQ0n3SeqJSdKi8Xk7c6Hddkmj8/tNJd2e873P1Uj/JC0k6TxJL0maIOkpSb/Kd29P0aakHSQ9rZR7f5SkrarUt4hSrv0xksZJ+o8Ka0YUyi0g6VxJr+dyD0jatXB8cOW3JD0dAPC8OllvogaV3P8PlvafSwpMTXoKIyImAEeS0vZcnHcfXjpvzbz9d2n/G/mc/xb2HRIR2wLvd9HHM0l36r/WUYGIuDMiTqpyqHJ9DwFIGtTFa2Cu79qIOLeD+saT0iHVex21WCNvfypprKTxku6RtHUP1G1mZmZmZmZmZj3IKYOsUSOA9UgTsJVJ5/uB9Ruo6w5g59K+fUl31D/aaAdJ+dm/DBxFys2+K2midnMASQNIk8ALA6eT8v7vBlwjaZ+IGNKNtou+nrdPlA9I+hUp9/1NpBQuxVz19fTvemAxUj79F4FvAH8EPsn7KhYg3Vl/MmnCey/gcknbRMTlud1FSL/vB8AJwHvAtsCVknaLiL/lcgsA9wHz5v49C2wFDJW0UET8IbdR+W23Iq2jcCAwNu8r3qlfq8F5W0l/8wUmp755GZhQKn8uaT2AdYE/R8RLpeOL5+0U+/Pd9TuU9k31G1YTEU91XWpqkhYnfdfXF/r5ahenvcDk76Rc31qkgMf/RcRHhf7VdB01WjtvZwL+AExP+lu7TNIPIuKSDvq2F2n8MXDgACbONVVcyqxpYvq5OxyT7e3tfdsZM2DcuHEee9ZSPCat1XhMWqvxmLRW4zFpRQ4IWKOeJN1dvCIpIPB+3le3iHgOeK7yWdKOpGDAcRFxWTf6+BVg9XyX+JmSlictPltxJGkyeLOIuCa3fRYpsHGSpCsjopFFUefOk/lzk4ImB5Mmy68vlZu0cG1EjKpST039kzQvsDxpsd4j8rnnSnoD+LBU50zAHpU7ySVdSFrw9gjg8kK7c5KCO6/nfcNId+T/AvhbodwiwIYRcXOu75x8rT8D/hARHwAX5GNLkAICwyJidPWvriZfyNvKtZ0F7Fg4vjs55z9AREyU9CywJNXH6Bx5Ww4k9ClJ05GCF0EaFxUbdHFq1X5Lmp0U/HmTqZ+K6Ek3AncVA1SS/o8UVPotUDUgkMsPAVhi8cExw7tX9GIXzeozca6t6GhMtm1T/qfcrPe1t7fT1tbW7G6YTeIxaa3GY9JajcektRqPSStyQMAa9RkpN3nlCYEH8r5uyXc0n0PK335IN6s7NAcDKh5lyrUONgOeq0y2A0TEx5LOIE0ybwCU87vX4vbS55HADhHxcWn/zMBPOwgG1Ny/iHhT0uPA9pIeI921/3ghOFA0Efh7ob7XJd0ObCzpCxExjpRTf1bgsSrnD5I0fUR8Sso//0wlGJDr+1TSl5n81Ehv+DRvZyA9UXEsKQCwAumJhilIWhv4HilodZikv0dEMVBSGbdT/XuYg0gfRMSzPdb7jh1NSm20b/EO/oi4pcH6/koKgmwaEWO7KtyoiLiwyr4PJA0FjstPi/yvt9o3MzMzMzMzM7PaeQ0B644RpCcEVszvuyXfQT6MNBG9U07ZUos5O9h/b+lzOWAxiFKamOzFwvFG7EOarF8fWDIiVu4ghczrwD86qaee/m0O3AX8CRgFvC3pwpzWp2hsREws7as8BVFZv2Ag6WmADaq8NiycNx8w1URvRHyWAwa9pTK5PV9u77E8ad5Reqk/khYW3paUfumnpeOV6y8uuktef+Eh0sLEvUrS90kBsHMj4sweqO/XwA+A30XEdd2tr0Hj8naRJrVvZmZmZmZmZmYlDghYd4wAlgOWJaWxaZjSwsTXkdLAbBoR47s4pehr1XbWcFf0GNIEcdnCheONuC8ibomI2yPi6U7KTegi6FFz/yLi6YjYLiK+CHyJlKd/C+D80rkDJc1U2jc/KU3N2/nzWGCWfA2TXqS0TuNIOeIhTaQvVO6cpO3zws5f6uTauuOhvP16aX+1vmxOWvT2uIi4EWgHDpE0d6HYAx3U9yXSkw4vd6+7nZO0LukJh3ZSMKm79f2AlMv/wog4urv1ddHWIEmX52soqzyN83qVY2ZmZmZmZmZm1gQOCFh3jOjgfV3yBPUVpAndTSPilQ6KjsvlFyycuwGwcoNNXwMsLmmjQn0zAj8h5WW/uaMT+0hN/ZO0kaSXJW0BEBEvRcTZ+fiqpTqnJy1MXKlvftI6Bw8WgjA3AF+VtEqh3PTAVcDVpLRDkAI4S0j6dqmNn5DuTn+ntL8SoJmL7rk4bw/Mefcrti8Wyn3+A2lR3kp++yOALwK/LhT9Byn10D6SZinsrywofFs3+9uhnM7oWvKCzBHxSTfr25aUEmo4sEf3e9ilt0lPwvwuf9+VfqxMWkz60bxGiJmZmZmZmZmZtQCvIWANi4jRksYC00XEc5LWabCqE4F1SRO9y0patnBsXEQMy+9vJ002Hy/pENJCumeTUgwVz6nV4cB3gMslnU5KxbMrKRf9vhHR7Duba+3ffcBspIWTv0a6o30ZUo7/8uqXE4A/S1qMdOf2PqS1DH5XKPM7Us792ySdBowmPW2wHPDjiPisUO67wFWFct8D1gaOjIj3Sm3fDHwEnCrp78DswFoRsV09X0pE3C1pCLAXcIOky4BvM2U6I0jf1TLAAZU1AyJiuKThwM8knRoRr0TE/yQdDJwCDJd0XuVaSYGuXlntVtJAUtBnduBC0joOxSKjOllfolp9y+R6PiUtEL1tqb576p2cLwSZhlU7HhEfSTqatHbDvyXdTUq9tFXuR7efeDAzMzMzMzMzs57jgIB11wi6P44qKX9+kF9FL5DWFSAiLpd0BLAn8CTwCGnSdyMaCAjkBXVXA47J9cxJyr+/eURcXfdV9LBa+5cXFV4TOJQ0ST6QNNl/Vt5X9DopndBxwKLAE7m+6wr1vSRpVdIit3uSgg3/BbaPiH8Uyr2ay/2BdDf6LMDjwPcj4tIq1/OkpC2B3wOnkyaM72jw6/kx8DQpQHQa8DCwO3AeQL7T/wjS0wF/KZ17BCm4dGS+PiLiVEkvkvL4nwy8R/r+DunuXfud+BaTn5Y4psrxI0m/d602A2bM70+rcnx3Utqnepyct8M6KhARJ0p6CzgI2Jf0JMhlwDER8WSd7ZmZmZmZmZmZWS9S7eu2mtm0TFI7MDgiBje5K2ZTWGqppeLJJx07sNbR3t5OW1tbs7thNonHpLUaj0lrNR6T1mo8Jq3VeEz2T5JGRsRUqda9hoCZmZmZmZmZmZmZWT/glEFm/UREtDW7D2ZmZmZmZmZmZtY8fkLAzMzMzMzMzMzMzKwfcEDAzMzMzMzMzMzMzKwfcMogMzNrqk8nfsRl53232d2wz7ltdr+h2V0wMzMzMzMzazoHBMzMphGSpgMWAwJ4PiLi89yumZmZmZmZmZn1rH6ZMkjSYElRer0m6WZJG/VhP0ZLaq+hXEga2vs96nlVvufxku7uy++5M63QP0kHSXpa0seS3pE0pK/a7g2Fv68jGjyv+Ho//12uVii3W6nMxPy3dKGkZUt1StL+kh6X9JGklyQdJ2nWUrnpJO0p6WFJEyS9KuksSXNU6eeiki6R9LakcZKuk7RM4fgRhb7NXdh/cmV/J9/BHJKerfb3LulHwOvAM8CzwEuStq5Sbl5JQyS9nq/lDklrdNRmV2ptt9brMDMzMzMzMzOz5umXAYGCe4GdgR8CQ4DlgOvyBFgr2Rn4S7M70Q0vkq5hV+BoYCBwraQtm9qryZrWP0k7AycA/wH2Bn4HvN3b7ba4kaTfY3fgz8AywHBJq5bKnZfL/QS4HtgKGCFp+UKZ44GTgTeBXwD3Ab8E/lGq61TSvwEjgf2BG0m/x9+KhSR9ifTvxoak3+13uX93S/pylWtZsfB+pU6vOjkNmKoeSVsBfwUuAr4BrA+MAS4pBkEkzQXcSfpe/pqvdU7g9mJQpVa1tlvrdZiZmZmZmZmZWXP195RBL0TEBZUPks4BniBNHJ7dtF6VFPs4jXq79D2fBTwFHAtc2bReTdbM/q2Vt7tHxMe93Na04uXS73EB8BhwFFBMNP+fUrl24FJgX2CfPEH/c1KwZf38/Z4m6RJge0nfiohbJQ3K51wWET/M1Q3JaXJ2lrRMRPw37/8zMD+wRkTcm9u9HnicFBzYrXQtKwLtkgSs0NlFS9qWFJSK0v7pScGHKyPiZ4X9uwMPA5vl7wfgMOCrwPYR8Y9c7kLgJeCPQFtnfehGu11eh5mZmZmZmZmZNV9/f0JgChHxAmmCa3CTu/K5FhFvkSZpl5L0xWb3p6yP+zdzbtPBgA5ExNOkdDWrd1H0+rwdnLebk/6NO6f0/VaCfVvk7aKAgFdK9Q0BTiEHTiXNm+u8sxIMyP17ghQQ+HaVPlWeClgMmKujjktamPQU0JWkJ1aKpic9rfDz0v5P8vazXMf0pIn4FyrBgNy/t4DhwJqSZumoD1XU1G4d12FmZmZmZmZmZk3mgEBBnlBbEHittP+7ku7M+d3HSrpR0kqF4w9JGpnfL5TzhI/InxdpJJ96qf0O1xDIudPbJS0o6XJJ70oaI+lUSTOWyu4i6VFJH+TtHpKukPShpLbS9d6Vc6S/k+tfq9x2N43P25lzm5PyzkuaVdKRkp6QdGmVa14392m8pDclnZ8nIlulf5sqrUMwXikX/aWSFi0cb9PkXPK75n3FnPiD66mvnv715HXkMpL0K0kv5HE0HFiqni+6RuPJv0UnFsnbyt9vJXXQo6Vyj+ftcnn7BPABsIuk9SqFIuKuiDggIkblXd8kBQf+XaXtS4D20r7bmZwyqLK9rXyiJJFSE30ATJWuLCI+joibI2J04Zzpgd8AE4Hr8u6lgAGklEZl1wCXA7NKmkfSoC5e09fRbk3XYWZmZmZmZmZmzdffAwIzSRqQX8sBQ4EFgPMrBSStD1wLzAocAvyelBv7Bk1eMPQ+YOk8IVZJC7KsUsqRSp7tapN0PWVu0h3An5Fyhj8F/BTYr3Adm5Im654n5UgfSbpT+l3S5N0zudwqpOudBfgt6ZpnJV1vZcK1W/Kk4gqkXPmvlw7PBtxFSuHyH+CO0rnbALcCg3L/ziTd6f0fpfzuze7fXsDVwBuk7/lUYJ3cv4G52OOkHO87k/K9U/i8cz63nvpq7l8PXwekNDR/JI25g4D/MXV+/m6R9AXgK6SJ+6LZ89/ufJLWZnK+/8rf73x5+2bpvMrngQAR8S6wJ+k7uU3SPZK+n/9+ixbP25fKfYyIoyJip9LuEcBXJc1MelJgTLVzSd9bG7BTvpu/Q0qL9f4JeATYGtgtIioBj876d2ZEfD8i3gauAF7t4jXF33oX7dZ9HWZmZmZmZmZm1hz9fQ2BLfOrYgJwEilXecWypLtr94mIMQCSnibdHbsGKU3JvcBepLQgy+djG5AmMZcl5dK+rxevYwVgSETsnft3Geku6W/l6wH4Tt7uEBHvA2dL2gSYr7RGwTqkVCF7RMTDub4bgF+R7j6uNqHZleklDSAFoBYCDgCWAI6OiHLakZ+QJvzXjIgPiwckzUoKALwMrBwR4/L+60mT2ycA2zWxf3MAfyL9/sU7pJ8l5bbfBTgxj6ML8jnfBtautk5ErfXV2r+evg6lFDoHAA8C342IT4EzlHP0d9J2V2bKv8cMpPQ/h5EWxj2tVO6E/KoYA/wwIm7NnyvpccrpmCqfZ63siIiLJT1Auvt9O+Bi4FBJ3y9MfM+RtxNqvI4RwIykfwNWBO4vF5C0AnAM8IeIGF5DnTOSFjFeiHSX/pyFY7X27yCgq1RYr5U+d9ZuI9dRCTrtBTBw4ACmn3PrWk4za1h7e3vNZceNG1dXebPe5jFprcZj0lqNx6S1Go9JazUek1bU3wMC7aRJrAHARcC/ImKKfNkRcRppIdIBkjYGVgZ2yIcrdyBXJvuXJQUE7ifdYbt83vd0vjO3t3xEmsis9PlNSa8CXyiUuYM0CbxHDhisSZoUfLpU192kAMaxks4AHoyI54F9utG/5Sjc9U6aUDwNOLJK2fdJdxhXm8xeg/RbnVoJBgBExD2S7gM2yalOPm1S/1YnTZRuXKqvotNFZXuovs76V2u5Wtv9JjATcGnpOz+b7gUEvldqdwJwWEScUyp3CulplvVI4/+YiDivcLxybTOVzqt8nmLiPCKeBHaV9Avg16Rgxz2SVoyI55icM3+qfzeVFjCeoxJEy0aRgg8r5dfZFNYnyQGui0hP61Qba1PJd95vLGku0hoH/yfppYi4tov+LUz623ksIkbW0lat7TZyHbnOIbkullh8cHz63uX1dsusLm1b31Bz2fb2dtra2nqvM2Z18pi0VuMxaa3GY9JajcektRqPSSvq7wGBMRFxC4CkrYGtJS1XTIUhaWnSIplrkyYQHyGlWFmyUM/jpNQ7lYDAZaT0HZWAQG+mCwJ4JiLKaVHKd7bfSkoFcgKTnxq4BTi8WCgi7pW0I3AgMAyYQdLzwBkRUb4bvVbPMjmgMAH4bycBkr9HxHsdHBuUt9WeUngRWI20cGu96Up6qn+VVDp/JH3fZeX0Q11ppL7O+ldruVrbnSdvXy0df7mG9jtzD2lcBikf/SMR8UGVck9ExC2S7iStxfBLSX8pLCA8Jm8HAk8WzhtYPC5pIVJw7MmI+CQi3gAOkvQk6W//YODHTA5SVEvVdC6wCjB7Yd/HwMOkxYYXIj0xMLhw/HjSU0XrAHOnjGNAelJl5vyUxPiIGE9JRLwraW9S+p4dSYGRzvp3NOk7mo/qQZ6adNBuw9dhZmZmZmZmZmZ9q7+vIVBUSRN0WGn/MNLk/zrAnBGxGunO5EkiIkiTfV8nLew5Kr9WIKXZ6M10QQBjayhzDumJiEHAWsCXI2KDnD99ChFxcUSsSprc/DppfYITJJVzpNdqXETckl93d/G0RGeThpUJ3moLCC9MuiN8quvpw/5Vfoe3C/XdkoNO7wOf1NmvRuqrddK1J66j8l2XJ6Dnr7EPHXkjt3drRNzbQTBgkoj4iBS8WBjYo3Cocrf+8qVTlsnbSuDvCFKgb8lSuYvzdnDePpC3X6/SjUWpHggZAXw/vy+nDNqUlLZoBGmSvvJaJJ/zBinIsYikP0laonhyRLxDepql8sTDQ6RAYEf9m0AdwbI62q3pOmpt18zMzMzMzMzMeo8DAllEPAJcCWwjaRmAnCN9KeDWiLgrIj7NCwfvWaWKe0l3zX5EuuN8FCnlyhfo/ScEarEi6S7dsXnS+/lqhSQNlfSUpBki4uOIeJD0tADAqn3V2Q7cQ5rQ3EPSpDuxJa1Gejrg+gbSBfWke0gT5j+QNGNlp6RVSWPgRx2d2Ef19XS7I4BPSX8zKpy/ey/1qzN/BV4Bfi2pMlF9Nal/e0gqPg1VCRoMy9tH8nbzUp3r5e1jAPlv4TFS6pyvVApJWp0UNLitSr9G5O1LlTVICnYkrTVSfo0Bbsrv/w68Q0r3dULxe5a0OTAzeWHq/JTQDcDXJa1TKLcoKd3W8Dr/Pmpqt47rMDMzMzMzMzOzJuvvKYPKjiItMnwoafHdNyW9DGwu6WBSCpDtSesIQGFRUtJTANMBj0ZESBqVP08gBQea7Q5SfvRK2qP3SHd4PxYRLxbKDSelFrlR0hWku8Er+eBv78P+TiUixkval5SvfISkIcC8wP6ku5APanL/3s/55/8C3Cfpb6TFWPcnpdk5pbPze7u+nm43Il6TdBZp0vh6SdeQAjPb9ka/uujzR5KOy33bjbTI9vOSTiAtiH2zpIuAdXP/roqIyng+D/gpcFReC+A+Usqvn5DG1cmFpn5ESqPULunPpInxg4G3geOqdK0SEJhqQeGIuLvatUj6EHi1ks4s7zuUtID07ZL+SXoK4+ekpyCKayv8jJS66GpJJ5JSLlUCelOkB+tKHgddtlvPdZiZmZmZmZmZWXP5CYGCvCDoVcD2kpbKuzchrRnwO9KE2kvA+qQ7jzcpnF55CmBUrutVUuqVkRExsfd736VzSTnZtyRNml4MXA+8IOn0SqG8KOuOwCykAMlJpPzqu0bElX3d6bKIuJR0x/FY4FjSpO01wKoRMbqJXQMmLZS6OSl90bGkVCn/BtYsBV6aUl8vtLs/6e9iOdJYWQpoNLVUdw0hrWdwSOHJhkNIfRwEnE4KCBzH5DQ+RMT7pDRaQ4GNcrltgUuAFSPi5ULZe0lBj5Gk6/4N6U751TsYf48D46gSEKhHRPyZ9L3OQcrZvxNwFrBe7n+l3LOkgMB1pEDAMcAzwDoR8Z/eatfMzMzMzMzMzKYNSunv7fMsBzf+SwoCDCdN8k5Hurt+b2DuiOhu3nczs4Yssfjg+OOhSze7G/Y5t83uN9Rctr29nba2tt7rjFmdPCat1XhMWqvxmLRW4zFprcZjsn+SNDIiVi7vd8qg/uEZ0t3+WwGbkdY1+BB4jZQv/sTmdc3M+rvpZ5i5rslaMzMzMzMzMzNrjAMC/UBeSPTI/DIzMzMzMzMzMzOzfshrCJiZmZmZmZmZmZmZ9QMOCJiZmZmZmZmZmZmZ9QNOGWRmZk01ceJH/H3od5rdDfsc22W3G5vdBTMzMzMzM7OW4CcEzMzMzMzMzMzMzMz6AQcEzMzMzMzMzMzMzMz6AQcEzPohSbNJmiDpqTrPC0lD6z3WSH19SdLo3Jdqr0tKZb8j6R5J4yW9JeliSYsUjrd1UldIau9mX4+XNLqT4z+U9JikjyV9JGm4pBUaaGe3Lq4jCmXX7qTcLA1eqpmZmZmZmZmZ9TCvIWDWP20AzAJ8RdKSEVFXYKADOwPP9kA9zTIWOLDK/tGVN5I2A64EXgYOB+YH9gfWkLRSRLwFPE76LgAGACcBdwJD8r4xjXZQ0kHAwcALHRw/ILc3HDgr9+8AoF3SMhHxah3N3cHk6yj7AzCh8Hl14BNgDyBKZT+uo00zMzMzMzMzM+tFDgiY9U+blt6f2N0KI+KC7tbRZB90dg2SBJwMfACsGREv5/3PAGeSggmHRcQY4IJ8bDBpgv657nw/+S77/wN2AN7ooMxA0kT9XyNir8L+Z4DzSJP7x9faZkQ8BzxXpZ3vAYsw5RhaA3goIs6vtX4zMzMzMzMzM+t7Thlk1s/kie2NgRtId79v2ukJVrEisBhwbSUYkP2ddHf85r3Y9neA9YE24L8dlJkDOAb4ZWn//Xk7f3c7IWl64ATgpoi4tnBoDdJTCWZmZmZmZmZm1sIcEDDrf1YBBgG3AbcDa0r6YrlQzkX/pKQPJY2UtHpnlXa1HkCt9Un6rqQ7Jb0jaaykGyWt1EG5uyS9ncu2S1qri2vvjuXz9tHizogYT0rhs7Skmp+6kjSPpEFdvKbPxUcCy0XEvR3VFxHPRcQxEfFO6dBqeftQrX3rxK7AMhSCDpK+AgwkpU16If++j0v6laQZe6BNMzMzMzMzMzPrIU4ZZNb/VJ4IuB14Ddgd+B5wUaWApB+TUtTcT5r8XRr4V6MN1lqfpPWBa0mT14cAMwH7ATdIWqoy2S1plUK535Ly1u+Wy301Il5qoJvTSRpQZf/bEfEpMF/+/GaVMm8CSwBzk9YiqMUVwLpdlFkMGF16IqFmeUL+56R1C4Y1UkehrumAXwH/ioiHC4fWztsBwNnAO6Tx9EdS//fpoL69gL0ABg4cwOxzbNOd7pl1qr29va7y48aNq/scs97kMWmtxmPSWo3HpLUaj0lrNR6TVuSAgFn/synwLvAgKSBQ2XcRTEoLc2Q+tm6+Ax5J75EmhOtSZ33LAtcA++Rc/Eh6GriOlJbm+lxuHWB6YI/K5LSkG3J9A4BGAgKLUD0//0qkwMMs+XO1RXI/yttZ62jvIGCqJzNKXuvieFcOB74K7B4R73ezrq2BJcmT+AVPk4IyJ1d+W+A0SX8DfijpyGqLGUfEEPJCy4svPjg+eP+ybnbPrGNbb31jXeXb29tpa2vrnc6YNcBj0lqNx6S1Go9JazUek9ZqPCatyAEBs35E0peAFYBbSJPRH5LS3XxX0gwRMRFYipQC5qzCBC+ku7/rDgjUU19EnEaaTB4gaWNgZdJCujD5Dn2Au0lPBRwr6QzgwYh4ng7uRq/RGGCnKvufydsP83amygFJAyJiLFBJjTOh1sYiYmQjnayVpO+SnrL4R0QM7YEq9wdGRcQUawVExJ3AnVXKnwHsQlr74MIeaN/MzMzMzMzMzLrJAQGz/mWTvP02U98NvzYpjdA8+XP5ru6G0tbUU5+kpYG/5L5MAB4B/kO6M32SiLhX0o7AgaRUODNIeh44IyJObLCfH0bELZ0cH5O3A3NfZwNuknQEacHeiaR0OU2Xv8eLSesd/LAH6lsSWBM4uI7TxuXtIt1t38zMzMzMzMzMeoYXFTbrXyrrB2wHbJBfvygdezdvB5bOnb/BNuupbxhp8n8dYM6IWA04pVqlEXFxRKwKzA58HRgOnCCp2l3+PaGSN7+yuPBnpGs6DhgMPJGfsGgqSQuQUit9CGwSER/0QLW7ka73ovIBSadLOrDKOZWFoF/vgfbNzMzMzMzMzKwHOCBg1k9Imh1YD3g8Iv4ZEbfkO+L/j3Q3fiUg8DjpTvfNJM1cqGL3BpuuqT5J85LSC90aEXdFxKeSBOxZ5VqGSnoqpzn6OCIeJD0tALBqg/3sVF6r4FlgY0kLR8SHpLURlib9W3p5b7RbjxwMuI0UqNikwcWVq9kOuC8iXqly7MvAIZImpXTKv+VhpPUWbuihPpiZmZmZmZmZWTc5ZZBZ/7EhMDNpgd5JImKCpNtIE91LR8QTko4B/gS0S7oI+AqwRyONRsTEWuqLiDclvQxsLulg0mTy9qR1BGDKBXuHA7sCN0q6Avgkl4WU9qgRs3fydMEl+e7/A4CrgOGSTmbKpxyGNNhuT7qMFKD4O/BVSV8tHBsTETcDSFodWBy4sqsnCHK6oMWBSzsochRwB3C/pKtJiz1vAQwCft5BEMHMzMzMzMzMzJrAAQGz/qPyBMD1VY5dB2ycyzwRESdI+gA4CDgReArYErixkYbrqG8T4M/A70iT/DcDvwTa87Ezc33nSfoI+AlpQnoW4Elg14i4spE+AgOA8zs4NgwYFxHXSvoecAQpVdBHwD3AGsBGpIWSm0LSoNwPSIv57lIqMpz0fQLsTQqoLAZ0lVLoe3lbNdCS13NYDziG9NTHR6R1H3apBCDMzMzMzMzMzKw1OCBg1k9ExA/pYIHZiDiTPNne2T5AndTf4bFa68tpeb5V5fSp/q2KiIuoktO+ERExuI6yNwE3VT5LmoEUIDivStnRdPKdNSIi2jrY/1qtbUXEbqR1AWopewodrONQKHM3ULVftZhhhpnZZbeGYk1mZmZmZmZmZlYHBwTMzLohpxI6tNn9MDMzMzMzMzMz64oXFTYzMzMzMzMzMzMz6wccEDAzMzMzMzMzMzMz6wecMsjMzJpq4qcf8Zfzv9Psbtg0Zu+dve6EmZmZmZmZWb38hICZmZmZmZmZmZmZWT/ggICZmZmZmZmZmZmZWT/ggIB9LkkaLSk6eF3S7P41k6QvS7pU0quSJkj6j6SN+rD9dkmje6pcF3WUf/sPJY2UtF136u0pkoZKil6qexVJ4yS1dVFuMUmvSdqtm+2d4783MzMzMzMzM7PW5jUE7PNsLHBglf2je7thSQcA70TE0N5uqx6SlgBGAG8DJwAB/Ay4WtIqEfFgM/vXS54Ajsnv5wR+CFwq6YOIuK6RCvPk+dwRcXKP9LCHSVoGuBaYvYtyCwI3APP3QLOrA7cD55b2j+6Bus3MzMzMzMzMrAc4IGCfZx9ExAVNavsA0kTo0Ca135GDgLmBtSLiMQBJVwNPAocALXHnfA8bUxwHks4HXgCOBBoKCAC7AYOBk7vZtx4naWvgPOCdLsqtA/wD+LgH2pwHWBo4vYl/c2ZmZmZmZmZm1gWnDDLrXwbn7SuVHRHxDHAscH8zOtTXIuJ94Bngq83uSy85EzgeOLyLcicDlwF79ECbqwMChvdAXWZmZmZmZmZm1kscELB+T9J3Jd0p6R1JYyXdKGmlDsrdJentXLZd0lqF40dU8qYDiwLrFvKoj65S3wqSrpX0vqT3JA3LqV6KZdrzeghz5hztYyW9JekCSXM2cLmVSf9TiudHxKERcXzpOnaV9GS+3h0kHZn7+bikJQt9/KakG3Lf3snf0foN9K1PSJqR9Pu8Vtrf6XVI2q3w+64LLFrMlV+lnSUl/TN/f+9KukfSJh30aSZJf8rrOryXx8WC+diskgZ18fpCobp1IuJoUjqozuwQEfsBn9TyvXVhDWAicGK+3vcl3dLV+gVmZmZmZmZmZta3nDLIPs+mkzSgyv63I+JTgDzhey3wECllzkzAfsANkpaKiHdyuVUK5X5LmmzdLZf7akS8BFxBuvMc4CTSGgaV3PXjih2QtAZwCymty9HA9MD+wH2S1o2IBwrFZyTleZ+Q294Y2BF4M59Tjz8C3wZ2BjaT9DfgxIh4sUrZI0h3kR9DSkFzH3Aa8BtgH+DnOYBxK+mJg6NIk8K7A9dK+lpEPFtn/3rDjIVxMA+p//OR+gtMyrnf1XXcQfreIP0OA6i+RgWSlgXuBj4ljYU3SOPlGkk7RcSFpVMuBL6c2/4msCvpTv/Nge1J339njiT9XkTEE12UpZ5yNVqb9N+TCaQnE+YEfgTcksfz3eUTJO0F7AUwcOAA5p19mx7sjvUH7e3tvVb3uHHjerV+s3p5TFqr8Zi0VuMxaa3GY9JajcekFTkgYJ9ni5AmYstWIk3sAywLXAPsExFjACQ9TcotvwZwfS63DmnSfo+IeDiXuwH4FWli+KWIGAWMyseOppS7vmQI6c7sVSLif/mcS4BHgbOAVQtlF8z1bhwRn0k6F3gJ+FbN30QWER9IWpu0sO7+pAWFfyzpOODwiPisUPzgiLhM0va5P9sBb5Em1OfJZb5GCmwcGhGP5utoBx4DvgP8X7197AVrMPU4OD4izih87vI6IuI54Ll87EfArJ38vicBswErRsR/8zl/JwWJfkoKABR9BVg9IiYAZ0pansm/743ABl1c43NdHO9tl5LWD/hHZYekIaTv7xBgqicjImII6e+AxZcYHG9+cFkfddU+L7bZ6sZeq7u9vZ22trZeq9+sXh6T1mo8Jq3VeExaq/GYtFbjMWlFDgjY59kYYKcq+yt38RMRpwGnSRogaWNgZWCHfHi+wjl3k54KOFbSGcCDEfE86U75ukhanBSIOLcSDMh9eU7SVcD3JS0YEa8UTju4MlkfEZ9Iegr4Ur1t5/MnkiZih0j6LukpgEOBL5Kejqh4KG8/Bl6LiNdy/4t1XQpcmlMabQB8A6jc6l38/pppFGkxZUh9+j7wS0mKiF9Cz16HpFmB9YHbK8GA3MYHpdQ+RYfmYEDFo6TAFRHxKvBqPX3oa6XgSmXf65L+CewhabpSsMnMzMzMzMzMzJrAawjY59mHEXFLldek9D2SlpY0HHgd+AfwPeA/5Yoi4l5Smp4BwDDgf5Kek3RQuWwNBuXtS1WOvVgqAzCuctd6Qd2Tq5JmlLScpIUq+yLiBmAV4CngJ5LmL5wysYP3xToXzEGMt0hPWmwPjKy3b73s7cJvf1FEbAbcDBws6RvQ49cxD+lpkv+VD0TEp5V0VSX3lj5/XibPx5HScLVKcMjMzMzMzMzMrF9zQMD6u2HAkqSUQHNGxGrAKdUKRsTFEbEqMDvwdWA4cIKkak8hdGZM3i5c5djCpTKQ1groCfMBj5CeCJgkIt4nrY8AMLjOOs8jrUmwOen7W4mUX7/Vjcrbr+RtT17HW6S1AxYqH5D0E0lDJc1W3B8RYxtsq+kkLSPpeklLVDm8Eik11jt92yszMzMzMzMzM6vGAQHrtyTNCywF3BoRd0XEp0r5cPasUnaopKckzRARH0fEg0xeUHbVcnlSrvi5qrUbEc8ATwDbSFqg0MZgYAvggWIqoR70Wu7X2pIqawAgaTpSQOQT0pMC9ViDlD7puoj4OO/buyc621skTU9aBBcmP5FR73WMBeZQMX9SllP/3A6smxcrrrQ7E/Bz0poEE8rnTcPeATYkpZ2aRNKmpHUQboqID5vQLzMzMzMz+3/27j3+snLu//jr3XRUFGaSiCmliLvc5IfEhG7pQCdnSSGiyCmnUNwOd0pSiRFy686pEiqd+3ZASVJIkkyFGiYdTE2n6fP7Y61v7XZ75nuY73f2bub1fDz2Y+99rWtd67PWvuY7j8f6rOu6JEnq4hoCWpKtvJCn979bVTcm+SvwiiQfoJkr/9U06wgArNRR/xxgF+DUJMfT3Dx/dbvt7B7t/wTYL8kBwOXAOjRT1xzcbt8dOA24KMmhNMm5d9OsUzDmdQlGo014fAw4ArggyVdpznlHmnP+bFXd1OMe98JcCjwnyf40CYeXc/9iuCstcK/F6zFtP1iGZpTE9jRJnIu4f6qesZ7HT2iu25FJzgMeCzy8qj7Sbt+bZt2Jc5N8iSaB8BqafrBrVdWEnuEkaq/d7Ko6vdf2qvp7kiOAPZOsRbP2xNo013AOTRJEkiRJkiRJA8CEgJZkU4FvL2DbCTTzm28DfAH4OM1N/tOBfYChdtsRAFX1zSR3Au8EPgmsCPwR2KWqftij/c+0dd5Ac7P4n8AnhjdW1XlJngd8GvgYTSLgbOAjPdYLmDBV9ZUkN9HcpP0UcCfNaIXXVtV3x9Hk64Ev0SQzpgDnAZsCZwAvS/KRBcyZvzhtwP394BZgFs10QId1LHQ7pvOoqm8lmQrsAbyxbffQju2/T/Jsmn7wXprf9zJgi6o6Y5LOc7J8myYh1jMh0NobuIYmmfV8mkWQvwp8umtxbEmSJEmSJPWRCQEtkapq+ijrXcr9T4J3etC/jao6BjhmlO3eDXy4fS2oziXAViO0M2Ms5aOM7XvA9xayfT9gvwUdq6rS8fkamnn3u/WcLqlXe4tab4Q2RjXcYZzncRBw0EK2XwnsNMJx3wS8abTlY1FVRwFHjaLeELDA6zSaa9gmSw5sX5IkSZIkSRpQJgQkSX217JQVeNvOp/Y7DEmSJEmSpCWeiwpLkiRJkiRJkrQUMCEgSZIkSZIkSdJSwISAJEmSJEmSJElLAdcQkCT11T3z7+QLx7y032FI93ncijvxhWM+2+8wtAR57+tcJ0WSJEnSYHCEgCRJkiRJkiRJSwETApIkSZIkSZIkLQVMCEhSnySZnqS6XrclOS/Ji3vUf1iSeUmunMAY3pGkFrJ9jySXtnH9M8npSV7Qo95aSb7T1rkpyWFJVpyoOCVJkiRJkrToXENAkvrvPGBm+3kq8A7glCSbVNVvOuptAawIrJfkyVW1SImBJK8CDl3I9vcBB7SxHQpMA3YFzkyyeVWd39abCpwLPBb4InAzsA/wSOD1ixKjJEmSJEmSJo4JAUnqv6ur6ujhL0mOB2YB+wI7ddTbtuvzQeM5WJJlgE8BHwRuANZcQNX3Ad+tqj069v06cD2wG3B+W/wJYDrw8qr6SVvvN8BPk3y1qs4dT5ySJEmSJEmaWE4ZJEkDpqquBeYATxkuSxJga+AUmmTBtj13Hp2nA7sD2wOnL6TeI4F/d5Xd0b7f1RHXK4HfDCcD2nM4BbiSByY0JEmSJEmS1EeOEJCkAZNkNeDRwG87ijcB1gDOonlCf+ckj6yqm9p9VgVWGqHpf1XVXcB1wFOr6p9JdlxI/VOANyY5DTiVZjqjLwABvtfWWR14DPB/Pfb/FfDMEWKSJEmSJEnSYmJCQJL6b4V2Hn5opu/5XPv5ix11hkcEnE0zzc+uwMuAY9ryQ4BdRjjO5sBQVf1rlHHtAhwPHNdR9m9gu6o6u/3+qPb96h77zwZm9Go4ye40oxSYOm0qj1vRgQQaHMsv80j7pCbU0NDQIu0/d+7cRW5Dmkj2SQ0a+6QGjX1Sg8Y+qU4mBCSp/17TvoYVsFfnFDw0CYFbgEtoEgLDZcMJgQOAo1m4S8cY147AC2mmKBqimULoZcCnk/y2qv7C/VPPze2x/+3Aqr0arqqZtAspP2nd6fW3O44dY2jS5Hncijthn9REevWMUxdp/6GhIWbMmDExwUgTwD6pQWOf1KCxT2rQ2CfVyYSAJPXfacDnaabieRzNgr2HJFmmqg5N8gRgI+AMmpvydwDXAFsmWbaq7qmqy4HLJyqgJI8CvkwzRdHLq+rOtnxD4Dzg68CLaG7608bebRlgxYmKSZIkSZIkSYvGhIAk9d/1VXXG8JckRwOXAV9I8n1gm3bTS4B/du27Gc00QhPtBTQ38788nAwAqKrfJzkO2DXJssDfaUY0rN2jjdVpRjVIkiRJkiRpACwzchVJ0uJUVffQPO2/LM2N9uH1A14FbNG+3t+WbfugBibG8BP/y/fY9jCa/z/SJgt+TzO1ULfn0CQMJEmSJEmSNABMCEjSgEmyMrBJ+/U6msWA/1BVP6iqM9rRBF8G5jF5CYHhRYLflGRKR2zPArYDLq2qu9vi44AXtNuG6z0f2BBYtImzJUmSJEmSNGGcMkiS+m+dJG8ApgBrAq8FnkBzo/3ZwArASZ07VNW8JGcBWyfZoKqumMiAqurSJGcDWwHXJvkdsBrwLJpk8gc6qh8G7A78KMkH2u0HArcBX5nIuCRJkiRJkjR+jhCQpP7bDPg2cBTNVEDzgHcBr+f+EQAn99hvOEkwWaMEtqVZ7HgeMAN4GvAr4JWdax5U1RzgpTTrG/wfzbkAvLqqrpqk2CRJkiRJkjRGjhCQpD6pqlncP1f/guzWvnrtfwRwxCLG8CbgTQvYdhuwT/saqZ3fJvlP4P8BDwd+XlVzRxPDslNW4L2vc2YhDY6hoSFePcM+KUmSJEla8pgQkCRNiKq6F/hFv+OQJEmSJElSb04ZJEmSJEmSJEnSUsCEgCRJkiRJkiRJSwGnDJIk9dXd8+9kv++/tN9hSPdZf7md2O/7n+13GH2z36tcP0GSJEmSllSOEJAkSZIkSZIkaSlgQkCSJEmSJEmSpKWACQFJ6pMk05NU1+u2JOcleXGP+g9LMi/JlYt43OXadrqPXUm27DreF5L8ua3/1yTfTjK9R5ubJjknyb+TXJPknYsSoyRJkiRJkiaeawhIUv+dB8xsP08F3gGckmSTqvpNR70tgBWB9ZI8uarGmxh4RtvO/sBVXdsu7fj8PeD5wCHAX4H1gN2BlyR5SlXdDE0yADgT+AfwSeDxwKFJ7qqqr40zRkmSJEmSJE0wEwKS1H9XV9XRw1+SHA/MAvYFduqot23X54PGebznAfcCB1XVv3tVSPJkYBtg986b+kkuAn4AvBz437b4CGAesGlVXdfWmwcclOSYqrptnHFKkiRJkiRpAjllkCQNmKq6FpgDPGW4LEmArYFTaJIF2/bceXQ2BX69oGRA65Hte3edO9r3u9q4ngo8HThqOBnQOgR4OPDSRYhTkiRJkiRJE8iEgCQNmCSrAY8Gbugo3gRYAzgLOBvYNMkjO/ZZNckaI7yWb6s/D1glyW/btQH+luTLSaZ1HO9S4Hpg/yTPSrJSkmcB/wPcCJze1nt6+35W5zlU1fXA34FnLvoVkSRJkiRJ0kRwyiBJ6r8VkkxtP68JfK79/MWOOsMjAs6mSRTsCrwMOKYtPwTYZYTjbJ7k2vYYKwLfopnuZyPgLcBmSZ5RVfdU1R1JZtDc6L+oo40rgBdU1Y3t90e171f3ON5sYHqvQJLsTrMeAVOnTWX95XbqVU3qixXyyKW6Tw4NDfU7BHWZO3euv4sGin1Sg8Y+qUFjn9SgsU+qkwkBSeq/17SvYQXsVVU/6SjbFrgFuIT7Rw5sy/0JgQOAo1m4S4FVgY8D36mq+xYUTnIh8HVgO+DYtngf4HE0CYHLgCcDm9GMGti5qu7g/pFmc3sc7/b2eA9SVTNpF1JeZ93p9ce7j+1VTeqL9ZfbiaW5T752xqn9DkFdhoaGmDFjRr/DkO5jn9SgsU9q0NgnNWjsk+pkQkCS+u804PNAaG7A7wYckmSZqjo0yRNonuI/g2Zu/zuAa4AtkyzbPtF/OXD5KI51E/CpHuXfBL5EM+f/sUleBrwZ2L+q9huulOS1NEmI3wKfpLnpTxt7t2VoRiJIkiRJkiRpALiGgCT13/VVdUZVnV5VRwEvAq4EvpDkMcA2bb2XAP9sX08EVqN5Yn+RVVXR3Nxfqy0aXgz44K563wGuamME+Fv7vnaPZlenGdUgSZIkSZKkAWBCQJIGTFXdQ/O0/7I0N9qH1w94FbBF+3p/W7btgxpYiCS7J/lGj/LHA9OAfwwXte/Ld9cFVuL+EWaX0Exx9MKu9qYB69AsLCxJkiRJkqQBYEJAkgZMkpWBTdqv1wGbA3+oqh+0IwnOAL4MzGOMCQGaUQW7Jtm843hTgAPbrz9u34cXCX5zV2xv5/51BaiqfwLnArsl6Vwv4G00SQUnI5ckSZIkSRoQriEgSf23TpI3AFOANYHXAk8AjgOeDawAnNS5Q1XNS3IWsHWSDarqilEeayawF/CTJN+nmdLnxcDTgROA49t63wE+DHw2yc40iYm1aRYWvhH4XEebHwfOAk5O8hma9Q4+TrPOwGmjjEuSJEmSJEmTzBECktR/mwHfBo6imQpoHvAu4PXcPwLg5B77DScJRj1KoKpuBjalGQnwCmB3mkWK3wHsWFX3tvX+ATyPZgHhR9EkDVZv43hJVc3uaPNcmumM1gNOBD4N/K5t767RxiZJkiRJkqTJ5QgBSeqTqprF/XP1L8hu7avX/kcAR4zjuNcCrxtFvatpkhKjafP4JKfQJBHmARdU1fzR7LvclBXY71XOLKTBMTQ0xGtn2CclSZIkSUseEwKSpAlRVbcDZ/Q7DkmSJEmSJPXmlEGSJEmSJEmSJC0FTAhIkiRJkiRJkrQUcMogSVJf3TX/Tt55/Jb9DkMPEYfvcEq/Q5AkSZIk6SHLEQKSJEmSJEmSJC0FTAhIkiRJkiRJkrQUMCEgSZIkSZIkSdJSwISAJPVJkulJqut1W5Lzkry4R/2HJZmX5MoJjOEdSWoh2//cI8ZK8vauehsmOSnJzUlmJ9k/if/HSJIkSZIkDRAXFZak/jsPmNl+ngq8AzglySZV9ZuOelsAKwLrJXlyVS1SYiDJq4BDF7L9McA6wJeBX3RtvrCj3pOBc2mSzAcCKwAfpPk/5qOLEqMkSZIkSZImjgkBSeq/q6vq6OEvSY4HZgH7Ajt11Nu26/NB4zlY++T+p2hu2t8ArLmAqs9t3w+vqssX0uRBwCOAZ1fVJe0x/gYcluTIqvrLeOKUJEmSJEnSxHI6B0kaMFV1LTAHeMpwWZIAWwOn0CQLtu258+g8Hdgd2B44fSH1NgX+AfxhQRWSPALYEjhxOBnQ+gYwrz2GJEmSJEmSBoAjBCRpwCRZDXg08NuO4k2ANYCzgOuBnZM8sqpuavdZFVhphKb/VVV3AdcBT62qfybZcSH1nwfcAfwyyVOB24BTgY93PPX/FJr/S87q3LGq7kpyGfDMkc5XkiRJkiRJi4cJAUnqvxWSTG0/rwl8rv38xY46wyMCzqaZ5mdX4GXAMW35IcAuIxxnc2Coqv41UkBJVgSeBdwDnAx8E1iXZmTBi5Ns2CYjHtXucnWPZmYD0xfQ/u5tW0ydNpX/t8zC8hLS/YaGhib9GHPnzl0sx5FGyz6pQWOf1KCxT2rQ2Cc1aOyT6mRCQJL67zXta1gBe1XVTzrKtgVuAS6hSQgMlw0nBA4AjmbhLh1DTCsDnwROq6qLhguTnAicCbyZZgHh4ann5vZo43Zg1V6NV9VM2oWU1153el1473FjCE1LszfOOGXSjzE0NMSMGTMm/TjSaNknNWjskxo09kkNGvukBo19Up1MCEhS/50GfB4I8DhgN+CQJMtU1aFJngBsBJwBPJJmGp9rgC2TLFtV97SL/i5s4d8xqaobgU/3KD8ryR+Al9IkBG5vN6VHM8sAK05UTJIkSZIkSVo0JgQkqf+ur6ozhr8kORq4DPhCku8D27SbXgL8s2vfzWimEVqc5gJrtZ//1r6vDQx11VudZlSDJEmSJEmSBsAyI1eRJC1OVXUPzdP+y9LcaB9eP+BVwBbt6/1t2bYPamACJNk+yY+TpKt8BZqFhP/RFv2Z5qb/C7vqTaFZg+DvkxGfJEmSJEmSxs6EgCQNmCQrA5u0X6+jWQz4D1X1g6o6ox1N8GVgHpOUEACWa9vuXqj408AqwI8Bqmo+cAKwYzu10bDX0qwfcOokxSdJkiRJkqQxcsogSeq/dZK8AZgCrElzM/0JwHHAs4EVgJM6d6iqeUnOArZOskFVXTHBMR1Ps4Dx15L8F3A98Nz2dQFweEfdzwA7Aack+SiwBs0ix9dz/6LHkiRJkiRJ6jMTApLUf5u1L4B/AVcB7wJmAke05Sf32O8kYGuaJ/knNCFQVfck2QL4FLAj8AjgT8BHgIOr6o6Oulcm2Qr4Fk0iAZqphF5fVf+ayLgkSZIkSZI0fiYEJKlPqmoWkBGq7da+eu1/BPcnDMYbw5uANy1g243AO9rXSO2cm+TJwPNopqP7eVXdOZoYlp+yAofvcMpoQ5YkSZIkSdI4mRCQJE2IqrobOKffcUiSJEmSJKk3FxWWJEmSJEmSJGkpYEJAkiRJkiRJkqSlgFMGSZL66s75d/KyH+3Y7zCk++zAVvzPjw7tdxhLhZ++4rh+hyBJkiRJSxVHCEiSJEmSJEmStBQwISBpYCSZnqS6XjckOT3JVosphllJhkZRr5IcNfkRTY4kuyX5fZK7ktyZ5JwkG/Wot2m77d9JrknyzgW0N6p6kiRJkiRJ6h+nDJI0iC4ADgeWA9YG3gqclOStVXVkXyO7387An/sdxHgk2Rs4GDgH+ArwGGBvYCjJU6vq+rbepsCZwD+ATwKPBw5NcldVfa2jvVHVkyRJkiRJUn+ZEJA0iK6pqqOHvyT5OnAF8H5gIBICnfE9lCSZBnwG+FpV7d5RfhXwTZpExwFt8RHAPGDTqrqurTcPOCjJMVV12xjrSZIkSZIkqY+cMkjSwKuqa4DfA9P7HMqS4OHAp4F9usp/1b4/BiDJU4GnA0cN3+RvHdK28dKx1JMkSZIkSVL/mRCQNPCSTAHWBG7oKNsyyXlJbk4yJ8mpSZ7Rsf03SS5uPz+unfP/ovb7Wu33/RYhpgWuITC8DkGSNZMcl+SWJLOTfCnJcl1135jkd0lua9/fnOT4JHckmdF1vucnuak956Ekz+/YvmqSNUZ4LV9VV1fVp6vq5q6wn9O+/6Z9f3r7flZnpXY6ob8DzxxjPUmSJEmSJPWZCQFJg2j5JFPb19OAo4DHAt8GSPIi4ERgJeDDwKeAdYBTkqzWtnEhsEGSAMOL5W6YZBlgw/b7BZN4DqvRzNF/L83T+FcCewF7DldIsi3wLeAvwLuBi2mmRLoFeAtwVVtvE5rzXRH4KM05r0Rzvmu1zR0CXD/C63m9Am2TFO8FZgMntMWPat+v7rHLbO4frTHaepIkSZIkSeoz1xCQNIi2b1/D5tEsgvvJ9vuGwE+At1fVbIAkfwJOornpfTLNzf7daRYl/o922xbAeu3+RZM0mCwbATOr6m1tfMfSjHB4cXsucP90Oq+rqn8DRybZBli9a42CFwBTgDdX1aVte6cAHwSmAtfRzPs/0roGly6g/BPAU4Bd2zjg/oTx3B71bwdWHWO9B0iyO83vw9RpU9mBrRYeubQYPZJV7ZOLydDQUL9DeEiYO3eu10oDxT6pQWOf1KCxT2rQ2CfVyYSApEE0RDPP/VTgGOCnVfXe4Y1VdShwaDuCYGvgWcDr2s2rt+/DN/s3pEkI/ApYq/28IfCnqrppEs/hTuAjHTHfmOR6YJWOOucC7wTe3CYMNgUeCfypq62f0SQwPpvkcOCSqvoL8PaO9i8HLh9rkEm2pBlx8P2qOqpj0+3DVXrstgzNaIWx1HuAqpoJzASYvu70Op6Txxa4NIl2YCvsk4vHT2cc1+8QHhKGhoaYMWNGv8OQ7mOf1KCxT2rQ2Cc1aOyT6uSUQZIG0eyqOqOqvgscB2zfTh0EQJINkpwD/AP4PvAy4JddbfyBZuqd4YTAZe1rOCEwmdMFAVxVVTd2ld3b9f1Mmql8DqR5yv+7bdknOitV1QXA62kSJCcAf0tydZL3LUqASTYAvgP8Dtita/Pf2ve1e+y6Os21HUs9SZIkSZIk9ZkJAUmDbniaoI91lJ0APJlmKp1HVNVzaObQv09VFXAR8J/A+tyfENgIeCqTO10QwJxR1Pk6zWiINYDnA+tU1RZV9aCb6FX1nap6NrAyzTmdAxyY5A3jCS7JY2mmVroD2KaqbuuqcgnNqIQXdu03jWa9hr+PsZ4kSZIkSZL6zISApIFWVb8FfgjslOSpSR5Nc4P/zKo6v6rmtwsHv7XH7hcAO9JM3/NnmoTA1jTT9kz2CIHR2Bi4varmVNXP2mmAHiTJUUmuTLJsVd1VVZcA72k3P3usB22TAWcB02iSAdd116mqf9JMabRbks51AN5GMz3QqWOpJ0mSJEmSpP5zDQFJDwWfpFlkeN+qel2SvwKvSPIB4C7g1TTrCACs1LHfhTSJz99VVSW5rP0+jyY50G/nArskmQf8FriVZoqd31fVtR31zgF2AU5NcjxwN805A5w9juMeC2wA/C/wlCRP6dg2u6pObz9/nCZxcHKSz9CMrvh4G+tpHfuMtp4kSZIkSZL6yISApIFXVZcm+RHw6iT7A9sAX6C56Xw3cDqwD830O9sAR7S7Do8CuKxt5/okc4ArquqexXcGC/QN4DU0yY630LEAb5LDq2pPgKr6ZpI7aRYg/mRb74/ALlX1w7EcMMkawPPar29sX53OobmeVNW5SV4FfAU4sd1+CfDqqrpreIfR1pMkSZIkSVJ/mRCQNDCqahbNNDO9tm3fVfTiHtUe8DetquZ0t1dV00aIYfpIcbb1esa5sDY6y5OsT7OA8HdobsLfQTN64dE00+28EtizY99jgGNGE9sIcd/AAq7xAuofn+QUmiTCPOCCqpo/3nqSJEmSJEnqHxMCktQfV9E87b8D8HKadQ3uAG4Afg4c1L/QHqiqbgfOmKh63VaYsgI/fcVx4wlNmhRDQ0P8dIZ9UpIkSZK05DEhIEl90D49v3/7kiRJkiRJkibdMv0OQJIkSZIkSZIkTT4TApIkSZIkSZIkLQWcMkiS1Fd3zr+brU54X7/D6KuTtxuYJSMkSZIkSdISzBECkiRJkiRJkiQtBUwISJIkSZIkSZK0FDAhIGlckkxPUl2vG5KcnmSrxRTDrCRDo6hXSY6a/IgmVsc1PqrHtqEk1VW2cZKTktyYZG6Ss5M8p8e+6yT5XpLrk8xL8stev1mS1ZPMTHJtkjuS/DbJLguJ93/beJ83zlOWJEmSJEnSJHINAUmL6gLgcGA5YG3grcBJSd5aVUf2NbL77Qz8ud9BTKYkmwJnAVcAnwAeAbwPOD3J+lX197beusBFwE3AgUAB7wJ+nGSTqrqkrfeott5KwCHArcBbgKOS3FRVP+46/hRgOKmwDfDzSTxdSZIkSZIkjYMJAUmL6pqqOnr4S5Kv09yUfj8wEAmBzviWYPu27zOq6iaAJBcAZwJ7A/u0298HrAY8v6p+39b7MfBH4MPAq9p6bwWeAGxTVSe19Y4G/tIe6wEJAeC5wKPbz9sCH5m4U5MkSZIkSdJEcMogSROqqq4Bfg9M73MoS5vpwO3ALR1l59M83X9lVz2Avw8XVNVVwGeBX41Q7ybgkzQjEbpt275/BXhakuk96kiSJEmSJKmPTAhImlDt1DFrAjd0lG2Z5LwkNyeZk+TUJM/o2P6bJBe3nx/XzkN/Uft9rfb7fosQ0wLXEBhehyDJmkmOS3JLktlJvpRkua66b0zyuyS3te9vTnJ8O7/+jK7zPT/JTe05DyV5fsf2VZOsMcJr+TGe5q9onvz/bJIVAKrqrqrau2vqpuGb/ockecRwYVXtW1UH9Kj3uSSrd9T7QlV9qMfxtwUuB77T8V2SJEmSJEkDxCmDJC2q5ZNMbT+vAXwQeCzw3wBJXgScCPyGZkqa5YE9gVPaue1vBi4E3pAkwEZtWxsmWQbYsP1+wSSew2rAOW2M+wBvAPaimR7n4PY8tgW+1Z7LF4HNaKZEOopmbv2r2nqbcP/5fpRmjv430ZzvU6rqOpqn9he4OG9rc2DWGM5hH+DZ7fsbkxwJHFJVc7rqfQ54Cc26Ci9P8i3goKq6tqveN2lu6r8CmJXke8CBw9MMdUryJOApNGtJXADMa/c9dEHBJtkd2B1g6rSp7MAzFlR1qTA0NNTvENRh7ty5/iYaKPZJDRr7pAaNfVKDxj6pQWOfVCcTApIW1fbta9g8mpvon2y/bwj8BHh7Vc0GSPIn4CTgecDJNDeRd6dZlPg/2m1bAOu1+xdN0mCybATMrKq3tfEdSzPC4cXtuQC8tH1/XVX9GzgyyTbA6l1rFLwAmAK8uaoubds7hSZRMhW4DjgAGGldg0uBVUd7AlV1fZKNaZIt76SZ5/89ST5YVYd31LstyWbAbsC7aRYU3iPJ/wCfqKp723r3AtsleRXwXpqkxi5Jvga8u6ru6Dj88GiAs6vqriQ/B16Y5OHtteoV70xgJsD0ddep47lktKe6RDp5xhv6HYI6DA0NMWPGjH6HId3HPqlBY5/UoLFPatDYJzVo7JPq5JRBkhbVEM3N+9fS3Lj/aVW9t6ruBqiqQ6tqe2B+kq2TfIL7b7IPT0UzfLN/Q5qEwK9oFib+j7bsT8ML5U6SO+lYBLeqbgSuB1bpqHNu+/7mJI9P8mrgkcCfutr6Gc11+Gx7vmtW1V+q6u1VdUnb/uVVdcYIrzGfb1XNq6rPA0+i+T1uAQ5L8v6uevdU1cyq2hB4GXA1TQLhSz3a/H5VPYcmeXMhTeLm2K5qwwmB37ejRX5FMxLkpUiSJEmSJGlgmBCQtKhmtzewvwscB2yf5GnDG5NskOQc4B/A92luQP+yq40/0Ny8Hk4IXNa+hhMCkzldEMBVbRKg071d38+kSRIcSPOU/3fbsk90VqqqC4DX04wGOAH4W5Krk7xvEuIGIMnKSZ42PNd/Vc1vf49NgDnAfkmmJFmurfe4jnhPaetdCbwzyWPaNp+WZO2Oer+gmSbpXGDrJP+vrbdqWw7N7/hPmtEQ4DoCkiRJkiRJA8WEgKSJNDxN0Mc6yk4Ankwzlc4j2qfND+ncqaoKuAj4T2B97k8IbAQ8lcmdLgiam+Yj+TrNaIg1gOcD61TVFlV1S3fFqvpOVT0bWJnmnM4BDkwy1nlhbm7fH95j28M7tm8M/Bb4QFccf6e5gb8yTYJi9bbeF7vq/Ztm3QOA6e37JTQJnM5693D/6IDhelsCy9Fcny06XrOBrdp1ICRJkiRJkjQAvFEjacJU1W+BHwI7JXlqkkfT3OA/s6rOr6r57cLBb+2x+wXAjjTT9/yZJiGwNc20PZM9QmA0NgZur6o5VfWzqvpLr0pJjkpyZZJlq+qudpqg97Sbnz2WA7YLLl8PPC/JfdMXJXk8TaJkeIHfK4B7gJclWb6j3sPaY/6D5sn9G2iSH5sleVRHvWVoEjZ304wUgCZx8LQk63SFtXn7Pnzs4VEAMzunPAJ+SpOEeO5YzlmSJEmSJEmTx0WFJU20T9IsMrxvVb0uyV+BVyT5AHAX8GrgWW3dlTr2u5AmSfm7qqokl7Xf59EkB/rtXJpFdefR3Cy/lWaao99X1bUd9c4BdgFOTXI8zU32V7fbzh7HcT8DHAqcl+SbNHPz7wGsCPw3NGseJDmIZqqenyX5X5q/77sAjwfeNrxYcJKPAUcAFyT5Ks1vsiPNb/LZjrULPgb8GDg/yeHAv2jWBHgF8J2q+l2SKTRTQP2DZoRHp5NoFiLelmZdBUmSJEmSJPWZIwQkTaiquhT4EfDqJOsD29CsGfBxmvn2rwNeBMxvtw0bHgVwWdvO9TRPs1/cTlXTb9+gWSx4e5opj74DnAxck+Sw4UpV9U2aNQRWpEmOHEyz+PAuVfXDcRz3cODdNNPyHNC2+S/gNe38/8M+DLwTWIFmnYMPAzcB/1VVMzvi+wrwmnbbp9r2VgBeW1WdCyufRJMA+CPwofbYT2iPsXNbbVPgUcAp7bRPnU6jSYa4joAkSZIkSdKAcISApHGpqllAFrBt+66iF/eo9oC/P1U1p7u9qpo2QgzTR4qzrdczzoW10VneJjbOpEkCnAPcQZNQfTTwNuCVwJ4d+x4DHDOa2EbS3mj/Uvsaqd6X29dIbX4P+N4o6p0BnLGQ7eey4D5wK81oBkmSJEmSJA0IEwKSNLKraJ6k3wF4Oc26BnfQzMn/c+Cg/oX20LfClOU4eTsvoSRJkiRJ0mQzISBJI6iq+cD+7UuSJEmSJEl6SHINAUmSJEmSJEmSlgImBCRJkiRJkiRJWgo4ZZAkqa/unH83W/3wv/sdhnSfHfIkDrBPaoDYJzVo7JOLz8nb79vvECRJ0hLGEQKSJEmSJEmSJC0FTAhIGpck05NUku/22DYryaxJOm61r3uT3JTk50n2TrLCZBxvUSQ5t411zR7bZiW5oUf5Ue0+0zvqDZ/zrUl+neTjSR6xkONukmRukhkL2P60JCcnuSXJPUmuTfKOBdR9a5LfJ7kjydVJ9kmSjnbuTfKNHvu9sY25Z7uSJEmSJEla/EwISBooSd6UZO8Rql0B7AJ8hmbqs4OBMxclKZBkuyT7jXf/Hu09Cnhe+3WbRWxuDrAz8HHgX8D+wIVJpvY47lOBE4GVFxDXk4Ah4D+BLwJ7AzcDhyd5e1fdTwMzgVnAu4BzgP9p96Oqfgd8D9h5OIHR7rcM8BHgGuDIMZ+tJEmSJEmSJoUJAUmD5k00N6kXZnZVfbuqPg/8P+BbwKbAPotw3O2ATyzC/t22Aqa0n7ddxLZuq6qjq+qLVfUS4FPABjQ35++TZEfgAuDOhbT1P8BtwDOq6hNVdRiwGXALzU3/4bb+A/gQcHJVbV1VM6tqV+C7wJ5J1mmr7gcE+HDHMV4FrA98sqruGu9JS5IkSZIkaWKZEJD0kFZVBbwfuAfYrc/hdNoW+DdwNPDiJCtNYNufpBk18Nqudo8ADmABiY12qp+zgN2q6vrh8qq6BbgKeExH9V1p/o84sKuZH7blL2r3/SPNOb4pyePbY3wU+BPwv+M9QUmSJEmSJE08EwKSJl2SjZKcmOTf7Tz4J7RT2wxvf9PwPPnAC4EndsybXyO1X1VzgEuB6UmmdbS7dpLvtGsN3J7kzCTP69g+o+MYu7Rl1fGa3patlGSNEV6rdLS7HLAlcB5wOrAS8JJFuogPPN97aKbvWQl4WsemF1TVfwM9r1k1vlxVZ3aWt7FvCPymo3jTtp0LupoZniZoTkfZ/jSjBD4EbN/GtH8bpyRJkiRJkgbEsv0OQNJD3vI95rJfBrgXoL0BfwbNPPX/TTONzrtp5sB/YVX9GjiXZo58aJ4unwq8Z4xxzAKeCTwe+GeSdYFfAMsBhwC3AnsAQ0m2q6qTgT90HHd3mqlzdu5o85/t+6uBb45w/P1pps8BeAHwCODs9gXNiIGfdO2THtdutOsgzGrfHw9cBFBVV4xy3257AysCX+koexJwY1XN66xYVZcDr+kq+0u7sPCbgRcDlwPfGWcskiRJkiRJmiQmBCQtqu3bV7dr2veZwN3AJlX1N4Ak36V50vwrwLOr6mrg6nbbW4CVquroMcYxfON6eDHdQ4BHtse9pG37GzQ3q7+WZHpVzaaZ7oYkLwE2W8BxTwW2GOH4V3d8Hl4z4Oyqui7Jn4FtkqSd4mjY6tyfdBir7vMdlyRPo0nCnFNVP+jY9HDghjE09d806z9sALyyqu4d4bi70yRhmDptKjvkSWMJW5pUq7GCfVIDxT6pQWOfXHyGhob6HcJDwty5c71WGij2SQ0a+6Q6mRCQtKiGgE93lQ3fZH8SzVQ03xhOBgBU1dVJfgS8JsmaVfX3CYhjxfZ9bpIVgf+iucl9Scdxb0pyFM3UNpsAPx9Nw+18+9ePWPF+2wJzgevaEQAXAq+jGcHwq456N9EswNvpA23sI7nvfMcQ1wMkeQRwbNvGzl2b76XH/xFJHgY8Gbi+TagAUFV/TXIhzeiI40Y6dlXNpEkWMX3dder4+vN4T0OacDvkSdgnNUjskxo09snF5+QZr+13CA8JQ0NDzJgxo99hSPexT2rQ2CfVyYSApEU1u6rO6CxIckf7cY32/boe+13bUWciEgJPaN//Cjya5u/bSMedcO3aCOu0X2d3bd6WByYE7upx7d4wykN1nu+YJZkCHEMzNdB/VVX3tfonsHqSZbqe9n82zTRI+wCf79qn4L6FniVJkiRJkjRgXFRY0mQaviH++B7bHt9VZ9ySrAZsDFzZLjD8L+CeyT7uAgxPF/RpmmmGtgC2auPZdkE7jUV7M/8FNE/2XzbOZg4DtgbeUVVn99j+a2B5mhEenZ7Yvo8rESFJkiRJkqT+MSEgadJU1VXAFcBOSR47XJ5kOrAd8OvOqYRac4CHJ8kYDnUAzc3rL7fHnUezkPELk2zUcdxVaea5v4F2Id6u4w7XWRTb0jwpf1hVndG+fgqcDzwjSa8kxVh9iGaEw5FVdddYd07yReDtwGer6msLqDa8lsJ9izu3v8lrgfnAOWM9riRJkiRJkvrLKYMkTbbdgdOAi5IcSpOIfDfNTfO396j/E2BH4Mgk5wGPBR5eVR/pqPOYJDsDj2nrPgc4Hji0o87eNGsEDCU5BLgV2INmId8detxIPxF4H3BUkh8DjwKeXFVvG+2JtusFPBe4uKq6F+Q9CZgBbEOzmPJYrNxOJfQomtEGL23P7aNjbIckb6W5/n8BLu8xRdEPq+q2qvpBkuOAXZOsRDNN0CvaY39xgtZ9kCRJkiRJ0mJkQkDSpKqq85I8j2YKnY/RJALOBj5SVb/rUf9b7Y31PYA3ArfwwBv9ABsA32q3XdLWO7pz7vqq+mOSZwOfoUkOLE+zuO+uVXV+j+MOJXkjzdP3XwHmAd8f4+luRZPwOLnHtpNo5tzflrEnBKYC3wb+DVwOvAv46nhGB9AkUADWbtvstjZwW/v5NcB7gbe0+11HkzQ5eBzHlSRJkiRJUp+ZEJA0LlU1C+g5rU9VTe/6fgnNzfLRtn0QcNACto16KqGq+jPw6jHU/za9b5KPdv//Bf53Adv+QMf16r5GHeVvopnWaKH1RojjKOCoBWzbcgzt3EMzHdMBo6w/Y7RtS5IkSZIkafEzISBJ6qsVpizHydvv2+8wpPsMDQ1x8ozX9jsM6T72SQ0a+6QkSdJDl4sKS5IkSZIkSZK0FDAhIEmSJEmSJEnSUsCEgCRJkiRJkiRJSwHXEJAk9dWd8+9h6+MP7ncYWsKctMN7+h2CJEmSJEkDxxECkiRJkiRJkiQtBUwISNKASzI9SXW9bktyXpIX9zs+SZIkSZIkPTQ4ZZAkPXScB8xsP08F3gGckmSTqvpN36KSJEmSJEnSQ4IJAUl66Li6qo4e/pLkeGAWsC+wU7+CkiRJkiRJ0kODUwZJ0kNUVV0LzAGe0u9YJEmSJEmSNPhMCEjSQ1SS1YBHAzd0lG3Zri1wc5I5SU5N8owe+26Z5PwkN7V1h5I8v0e9TZOcluTWtr2Tkzx9vO1JkiRJkiSpf0wISNJDxwpJprav/wCOacu/CJDkRcCJwErAh4FPAevQrDOw2nAjSTZp660IfLStu1Jbb62OelsBQ8AKwAeBzwDrAhcmecpY25MkSZIkSVJ/par6HYMkaSGSTAf+0mNTAXtV1eFtvb2AFwFvr6rZbdlWwEnA1lV1clv2PuBAYOOqurQtW5vmpv9Xq+qSJAGuAm4FXgrc2x5zPeDnwGFVtddo2+txTrsDuwNMnTbtmZ+dedj4L5DUw7qrrT7ufefOncsqq6wygdFIi8Y+qUFjn9SgsU9q0NgnNWjsk0unzTff/OKqelZ3uYsKS9JDx2nA54EAjwN2Aw5JskxVHVpVhwKHtiMItgaeBbyu3bfz7ujPaJIJn01yOHBJVf0FeHtHnfVoRhcAzO4Ry0ZjbO8BqmomMBNg+rpPqh/e+7eRz14ag5NmvGrc+w4NDTFjxoyJC0ZaRPZJDRr7pAaNfVKDxj6pQWOfVCcTApL00HF9VZ0x/CXJ0cBlwBeSfB94JPBVYDNgHvBb4JfAkzsbqaoLkrweeA9wArBskr8Ah1fVQW21ae37UcD/9Yjl1jG2J0mSJEmSpD5zDQFJeoiqqnuAy2mSu2vT3Ix/MvAC4BFV9RzgkAXs+52qejawMvCfwDnAgUne0FaZ077Pq6ozOl/ttrvG2J4kSZIkSZL6zISAJD1EJVkZ2KT9eh2wPnBmVZ1fVfPbdQDe2mO/o5JcmWTZqrqrneP/Pe3mZ7fvV9KsW7BdklU79l0TuAj42BjbkyRJkiRJUp85ZZAkPXSs0z5xPwVYE3gt8ATguKr6W5K/Aq9I8gGaJ/hfTbOOAMBKHe2cA+wCnJrkeODuti7A2QBVVUn2BH4EXJxkJs00RHu0bX96LO1JkiRJkiSp/0wISNJDx2btC+BfwFXAu2gX5wW2Ab4AfJzmpvzpwD7AULvtCICq+maSO4F3Ap8EVgT+COxSVT8cPlhVnZxkc+ATNCMC7gYuBHauql931BtVe5IkSZIkSeovEwKSNOCqahaQUdS7FHhxj00P+ltfVccAx4yizfOBLUZRb1TtSZIkSZIkqX9MCEiS+mqFKcty0g7vGbmiJEmSJEmSFomLCkuSJEmSJEmStBQwISBJkiRJkiRJ0lLAhIAkSZIkSZIkSUsB1xCQJPXVnfPvYevjj+h3GJpEJ+2wR79DkCRJkiRJOEJAkiRJkiRJkqSlggkBSZIkSZIkSZKWAiYEJE26JNOTVNfrhiSnJ9lqMcUwK8nQKOpVkqMmP6KJNZZrnGS5JPN61K8kW3bV3TTJOUn+neSaJO9cwPFHVU+SJEmSJEn94xoCkhanC4DDgeWAtYG3AicleWtVHdnXyO63M/DnfgexCEZzjZ8BrAjsD1zVtf+lwx+SbAqcCfwD+CTweODQJHdV1dfGWk+SJEmSJEn9ZUJA0uJ0TVUdPfwlydeBK4D3AwOREOiM7yFqNNf4ecC9wEFV9e+FtHUEMA/YtKqua9ubBxyU5Jiqum2M9SRJkiRJktRHThkkqW+q6hrg98D0PoeyxFrANd4U+PXCkgFJngo8HThq+CZ/6xDg4cBLx1JPkiRJkiRJ/WdCQFLfJJkCrAnc0FG2ZZLzktycZE6SU5M8o2P7b5Jc3H5+XDvv/UXt97Xa7/stQkwLXENgeB2CJGsmOS7JLUlmJ/lSkuW66r4xye+S3Na+vznJ8UnuSDKj63zPT3JTe85DSZ7fsX3VJGuM8Fp+IefzoGtMM0JglSS/bdcS+FuSLyeZ1lHn6e37WZ3tVdX1wN+BZ46xniRJkiRJkvrMhICkxWn5JFPb19OAo4DHAt8GSPIi4ERgJeDDwKeAdYBTkqzWtnEhsEGSABu1ZRsmWQbYsP1+wSSew2rAOTRT7uwDXAnsBew5XCHJtsC3gL8A7wYuppmu5xbgLbTz9ifZhOZ8VwQ+SnPOK9Gc71ptc4cA14/wel5HfCNd43VoEgSrA6cD72tjeAtwVpLhqeQe1b5f3eMazOb+EQejrSdJkiRJkqQ+cw0BSYvT9u1r2DzgYJqFaKG5of8T4O1VNRsgyZ+Ak2huep9Mc7N/d5oFc/+j3bYFsF67f9EkDSbLRsDMqnpbG9+xNE/fv7g9F7h/mpzXtdPyHJlkG2D1rjUKXgBMAd5cVZe27Z0CfBCYClwHHACMtK7BpcCq7eeRrvG9wMeB71TVfQsKJ7kQ+DqwHXAs9yeM5/Y43u0dxxttvQdIsjvN78jUadPYfpmpvc9MS4ShoaF+hzAmc+fOfcjFrCWbfVKDxj6pQWOf1KCxT2rQ2CfVyYSApMVpCPg0zc3uY4CfVtV7hzdW1aHAoe3T7VsDzwJe125evX0fvtm/IU1C4FfAWu3nDYE/VdVNk3gOdwIf6Yj5xiTXA6t01DkXeCfw5jZhsCnwSOBPXW39jCaB8dkkhwOXVNVfgLd3tH85cPlIQSUZvvE+xMKv8SyakRfdvgl8iSaZcSzNzXyA9Ki7DM2oBsZQ7wGqaiYwE2D6uk+qH947p1c1LSFOmvHKfocwJkNDQ8yYMaPfYUj3sU9q0NgnNWjskxo09kkNGvukOjllkKTFaXZVnVFV3wWOA7Zvp7UBIMkGSc4B/gF8H3gZ8MuuNv5AM/XOcELgsvY1nBCYzOmCAK6qqhu7yu7t+n4mzVQ+B9I85f/dtuwTnZWq6gLg9TQ3708A/pbk6iTvW4T4FnqNF6Sqiubm/vBURX9r39fuUX11mt9gLPUkSZIkSZLUZyMmBNrFM1ftKtsuyVaTF5akpcDwFDYf6yg7AXgyzVQ6j6iq59DMoX+f9sb1RcB/Autzf0JgI+CpTO50QQCjeZT96zRP6q8BPB9Yp6q2qKoH3Ryvqu9U1bOBlWnO6RzgwCRvmIBYH3SNk+ye5BvdFZM8HphGk4wBuIRm9MILu+pNo1nX4e9jrCdJkiRJkqQ+G80IgVcC5yU5NMnDkpwBvAT47eSGJmlJVlW/BX4I7JTkqUkeTXOD/8yqOr+q5rcLB7+1x+4XADvSTN/zZ5qEwNY00/ZM9giB0dgYuL2q5lTVz9ppgB4kyVFJrkyybFXdVVWXAO9pNz97UYPovsZt8WrArkk274hjCs1oBoAft/v+k2bqo926ksJvo5ke6NSx1JMkSZIkSVL/jXYNgbcC84ENaOaY/iswNcntPabOkKTR+iTNArj7VtXrkvwVeEWSDwB3Aa+mWUcAYKWO/S6kSWj+rqoqyWXt93k0yYF+OxfYJck8muTprTRT5/y+qq7tqHcOsAtwapLjgbtpzhng7AmK5b5rTLMew0xgL+AnSb7fxvVi4Ok0IzSO79j348BZwMlJPkMzCuPj7TmdNo56kiRJkiRJ6qOFjhBI8lJgCs10EC8BtqV58nUb4BV4o0fSIqiqS4EfAa9Osj7N35Zf0txM/gTN/PsvoklIbtOx6/AogMvadq6nmcrn4qq6Z/FEv1DfoPm7uT3NlEffAU4Grkly2HClqvomzRoCK9LcuD+YZvHhXarqhxMRSPc1rqqbaRY5/jHN3/HdgTuAdwA7VtW9HfueC7wKWA84kWax4t+19e4aaz1JkiRJkiT11wJHCCR5OM0NuM2ATWieKv0j8CHgy1V1Rbv45TKdN5AkqVtVzaKZPqbXtu27il7co9oD/lZV1Zzu9qpq2ggxTB8pzrZezzgX1kZneZvYOJMmCXAOzc32ZYBH00yj80pgz459jwGOGU1sI8Q9i1Fe43aUwutG2e7xSU4BnkczAuOCqpo/3nqSJEmSJEnqnwUmBKrq30k+V1V7JflPmukmXgP8CjggybVVteeC9pekpdRVNE/77wC8nGZdgzuAG4CfAwf1L7TxqarbgTMmql63FaYsy0k77DGe0CRJkiRJkjQGI60hcGCSjwCXAGvRTBe0ZVW9PMnDJjs4SXqoaZ+K3799SZIkSZIkSQNjpITAD4Fjgb8DzwBOBS5K8mMgSR5RVS+c5BglSZIkSZIkSdIiWuiiwsClwE9pFp78FbAXcDtwEbCTyQBJkiRJkiRJkh4aRhohcDVwBFDAJ6vqZODoJNsBj2u3S5I0bnfOv4dtjvtmv8NYqBN33LXfIUiSJEmSJC2yhSYEqupu4G/t15M7yk+YxJgkSZIkSZIkSdIEG2nKIJJM7fp+YJLPtK+DJi+0pVeS6Umq63VDktOTbLUY45iVZGgU9SrJUZMf0cRawHW+Lcl5SV68kP0Wy/l2xPfdHttmJZnVz/j6Zby/W78M2u+xgOvX+ZrV7xglSZIkSZI0OUaaMgjgd0neVFWntN9nAO8DDgLeO1mBCYALgMOB5YC1gbcCJyV5a1Ud2dfIHmhn4M/9DmIRnAfMbD9PBd4BnJJkk6r6TY/6g36+I8aXZG/g5qo6aiLq9clYf7cRTdJ1GbT+8k+amIZ9G7gC+HT7fe5ij0iSJEmSJEmLxWgSAlcAWyZZt6oOA/5VVeckuamqzp3k+JZ211TV0cNfknyd5vd4PzAwCYHOGB+iru66zscDs4B9gZ26Kw/6+Y4yvr1pzvGoCarXD2P63UZpbyb4ugxaf6mq24DO6/ZtYPagxSlJkiRJkqSJN+KUQcBdVbU38NQkO05yPFqIqroG+D0wvc+hLNGq6lpgDvCUfsei0fN3kyRJkiRJkhZuoQmBJM9s31cDngHs2bG5Ji8s9ZJkCrAmcENX+Zbt/Ok3J5mT5NQkz+jY/pskF7efH9fOE35R+32t9vt+ixDXAudIH16HIMmaSY5LckuS2Um+lGS5rrpvTPK7dj743yV5c5Ljk9yRZEbX+Z6f5Kb2nIeSPH+88feIeTXg0XRd59Gc71jOY7IsKL4k+w3PEw88EXhhr3njR1uvo/6mSU5Lcmvb/05O8vQFxZVkSpL3JLk0yc8n8LxXo8fvNop/H5NyXbrPewHbJvzfx0Qbw+87qnqSJEmSJEnqnwUmBJI8CvgUsAbweuBDNNNkrJ3k48CTknw8yWhGGWh8lk8ytX09jWZ6ksfSzPkNQJIXAScCKwEfpvnN1qGZS321ttqFwAZJAmzUlm3Y/nYbtt8vmMTzWA04B7gX2Ae4EtiLjgRTkm2BbwF/Ad4NXEwzLdItwFuAq9p6m9Cc74rAR2nOeSWa811rnPGt0HGd/wM4pi3/4lgbGu15jFFnP5iaZqHv8fy7O55m7vidaZ6kv6Lj+97jqEeaRa6HgBWADwKfAdYFLkzS60n9ZYAfAfsDlwOnjuM8ho34u43y38eEX5cxWo0J+vcx0Ub7+46jH0iSJEmSJKkPUrXwB/2TvB54E7BDVf07yUnAl4GbgRWq6qzJDnJpk2Q6zY2/bvOArwAfrKq727p7AS8C3l5Vs9uyrYCTgK2r6uQkuwLfAJ4EvAp4PrAF8B/ANsDngUdX1U1dccwCZlXVjBHiLeBbVfWmHttm0TxNPbOq3taWDT/FfWpVbdOWHQa8E3hEVf27LbsRuKCqtu5o733AgcDGVXVpW7Y2zU3Ir1bVJQuLtSu26fS+zgXsVVWHj+N8R3UeixjfsGuqavpY4uuoM4vR/bYLrNcmmK4CbgVeSnNDG2A94OfAYVW1V1dctwO/A7bs7m+jNZbfbTT/PrransUiXpcedRfbv4/xaOM7p/tcRvv7jrUfdLS/O7A7wNRp05752ZlHLMppTLp1V3t0v0PQYjR37lxWWWWVfoch3cc+qUFjn9SgsU9q0NgnNWjsk0unzTff/OKqelZ3+YiLClfV/yW5BdgDOIDmKdVVq+qkiQ9TXYaATwNTaZ5+/mlVvbezQlUdChzaPiW9NfAs4HXt5tXb9wvb9w1pkgC/AtZqP28I/Gm8N2dH6U7gIx0x35jkeqDzL9G5NDc835zkWGBT4JHAn7ra+hnNjd/PJjkcuKSq/gK8fRHiO40mKRLgccBuwCFJlmmv71iM9jzGYoimH3QahAVg16N52h5gdo/tG/UoWwZ41QT1txF/t1H+++i3ifz3MZFG+/uOpx9QVTOBmQDT131SnTD/1vFHuhicOMMldJYmQ0NDzJgxo99hSPexT2rQ2Cc1aOyTGjT2SQ0a+6Q6jZgQAKiqE2mm3YDmJtwjJy0idZpdVWcApFnQecckT6uq3w1XSLIB8FVgM5oRBL8Ffgk8uaOdP9BMLTKcEDiWZrTAcEJgMqcLAriqqm7sKru36/uZwPU0T/8f3JadAXyis1JVXdCOWnkPcAKwbJK/AIdX1UHjjO/64esMkORo4DLgC0m+P/xk+SiN6jzGaHZnfG2MdyxCexNlWvt+FPB/Pbb3usP7k3Zx7Ikw4u82yn8f/TZh/z4m2Gh/3/H0A0mSJEmSJPXBqBICcN/0ETtW1bE00wVp8foksCPwMeDVHeUnAKsCLwB+UVXzkzwLeMNwhaoaXkT4P4H1aW6aPonmJulTaUZ9TKY5o6jzdZon4d9FE+Pf2yf/H6SqvgN8J8nyNAmNdwEHJpldVYv85HxV3ZPkcuApwNr0fup5QUZ9HkuA4d91Xo+ExcYL2Of2yQpmAb/bCYzw72MATOi/jwk02t93PP1AkiRJkiRJfTDWhUl3BUiy0iTEooWoqt8CPwR2SvJUuG+u8fWBM6vq/PZmZ4C39mjiApqEwp3An2mSAlvTTEsy2SMERmNj4PaqmlNVP1vQzc4kRyW5MsmyVXVXu2bAe9rNz56IQJKsDGzSfr1ujLtvzCjOYwDMoblRvij1rqSZy3+7JPfVSbImcBFN8mqx6f7dxvjvY9hEXJfJsDGLv1+N9vcdqH4gSZIkSZKkBVvoCIEkf6B5yvbRNDdb57ebZif5I3B0VR0yuSGqwyeB7YF9gde1c43/FXhFkg8Ad9GMHhheLKIzcXMhTQLod+2Igcva7/NokgP9di6wS5LhaV1upZnm6PdVdW1HvXOAXYBTkxwP3M39IybOHuex10nyBmAKsCbwWuAJwHFV9bdJOo9++wmwX5IDgMtp5oC/qaoOHm29th/tCfwIuDjJTJr+tAdNX+xe92Cijfi7jeHfx7BFvi4TeYIdFnu/Gu3vOwD9QJIkSZIkSaM00pRBf62qLZJ8mweOJrgY+C+aJ8tNCCwmVXVpkh8Br06yf1X9EdgG+ALwcZqb46cD+9BML7INcES7+/AogMvatq5PMge4oqruWXxnsUDfAF5Dk/B4C7Di8IYkh1fVngBV9c0kd9IssPrJtt4fgV2q6ofjPPZm7QvgX8BVNFOzzJys8xgAn6GJ7Q3AY4F/0ns++oXWq6qTk2zeln2Mpg9eCOxcVb+ezBNgdL/baP99DJuQ6zIJ+tKvRvv79rkfSJIkSZIkaZRGSghU1/t95VV1d5KHJUlVdW/XIqiqWUAWsG37ru+XAi/uUXXZrnpzutusqmksRFVNHzlaqKqesS6sjc7yJOvTLJr6HZoRAHfQJKAeDbwNeCWwZ8e+xwDHjCa2EeKexQKu8wj79dxnrOexKPEt7LdZ2O/RUedu4MPta5HqVdX5wBajOOaYr/UC2pnFKH+30f776Kg/Ydelo+5i/fcxViP9LmP4fUdVT5IkSZIkSf0z6kWFF2ALkwGaAFfRPO2/A/BymnUN7gBuAH4OHNS/0MZkSTkPDZYlvl+tMGVZTtxx136HIUmSJEmStMQbKSGQJMtw/9O4z09y9PDGqvrrpEWmpUZVzQf2b18PWUvKeWiw2K8kSZIkSZI0UUZKCCwPnEazaOd84Gc0U2SsN8lxSZIkSZIkSZKkCbTQhEBVvXD4cztSYPmqug64brIDkyRJkiRJkiRJE2csawgUS8Bc1ZKkwXLn/Plsc+z/9TuMCXfiTq/vdwiSJEmSJEkPsMzCNrajAgCoxmld2985WYFJkiRJkiRJkqSJM9IIgVOS3NF+DvBYYD9grao6AtgBOHzywpMkSZIkSZIkSRNhoSMEgJ2BvdrX+4C3A7cDr2y33zt5oUnSgyWZnqS6XrclOS/Ji/sdX7c2vqP6HcewJDPamP6SZErXtkoyq0+hSZIkSZIkaZKNNELgpcAmNOsHLFdVewAk+VC7vSYxNklamPOAme3nqcA7aEY1bVJVvxlPg0n2Bm6uqqMmol5rZ+DP44lnkk0HXgV8Z6IaHON1kSRJkiRJ0mK2wBEC7foBT6qqvYAVgI+35TsCj0/yDpophCSpH66uqqPb1xeBlwBTgH0Xoc29gTdNYD3a+H4x7ogm1wcmuL29GeV1kSRJkiRJ0uK3wBECVXVvkj2SPBd4KrB+kuVonia9E/hb+y5JfVdV1yaZAzyl37E8RNwOPCPJFlV1er+DkSRJkiRJ0uQbaQ2BC6rqv4CfAjtU1aZVdRjwz6r6EXDTpEcoSaOQZDXg0cANXeVbtusL3JxkTpJTkzyjY/t+w2sRAE8EXtixNsGssdbrEdcC1xBIMivJUJI1kxyX5JYks5N8qU3AdtZ9Y5Lftesl/C7Jm5Mcn+SOJDPGdrUAOJomqbvPwiol2TTJaUluba/fyUme3rF9XNdFkiRJkiRJi99Iawg8O8mPgI2A6Um+VlXfp5lCCFxDQFL/rJBkavt5TeBz7ecvDldI8iLgROA3wIeB5YE9adYaWL+qbgaOB65qdzkYmAN8uv0+t+N4o603VqsB57Qx7gO8gWYh97+0xyHJtsC32nP5IrAZcCRwFPCWjrjG4nrgf4G3JnlGVV3SXSHJVsCPgJ8DHwRWollc/sIkz6yqPzDO65Jkd2B3gKnTprHdlJXGcQqDbWhoqN8haJzmzp3r76eBYp/UoLFPatDYJzVo7JMaNPZJdUrVgu/pJ/lBVb0yydeBL9HcEPol8Leq+kGSoaqasXhClSRIMp3mZnm3AvaqqsM76u4FvAh4e1XNbsu2Ak4Ctq6qk7vangXMGunv2mjrtXUL+FZVvWkB7TwRmFlVb2vLhkc5nFpV27RlhwHvBB5RVf9uy26kGcW19UgxdB1zBnA2sD/NKIErgB9U1WvbWK+pqulJQnOj/1aaBebvbZtYjyZBcFi7xkz3+YzqunSavu669bTP7T+WXR4STtzp9f0OQeM0NDTEjBkz+h2GdB/7pAaNfVKDxj6pQWOf1KCxTy6dklxcVc/qLl/oCIGqemX78RjgSponUR9ZVX9ryw+a0CglafROAz4PBHgcsBtwSJJlqupQgPb90CRTk2wNPAt4Xbv/6n2IuZc7gY8Mf6mqG5NcD6zSUedcmoTAm5McC2wKPBL406IcuKquSnIc8MokH+7avB6wTvt5do/dN1qUY0uSJEmSJGnxG2nKIACq6ky478nVO5I8CpgCnDmJsUnSwlxfVWcMf0lyNHAZ8IUk36+q2Uk2AL5KM8XOPOC3NKOcntyPgBfgqqq6savs3q7vZ9JM8XMg7TRCwBnAJybg+J8DXgW8r6t8Wvt+FPB/Pfa7dQKOLUmSJEmSpMVoVAmBDj+kmUIiwJbAzjQ3pSSpr6rqniSXA08B1qZ5qv0EYFXgBcAvqmp+kmfRzNM/KOaMos7XgSHgXcD6wN+rqte0SWNWVZckOY1mhEWvuOZ1Jl4Akmw8EceWJEmSJEnS4rXQhECSmTTTWSwLHEGz5sBu7bbzum8SSVK/JFkZ2KT9el07oml94P+q6vy2ToC3LqSZOTQJhJGMtt5E2Rg4o6rmMLoEwlh9Djirq+xKmrUatkvy4aq6BSDJmsBFwI+BHbv2WdzXRZIkSZIkSWMw0giBpwOvAV4NrAWQZC1gGWCFJGtV1XWTG6Ik9bROkjfQTF+2JvBa4AnAccPrnCT5K/CKJB8A7qL5Wza8mMpKPdr8CbBfkgOAy2nm0L+pqg4eZ72Jci6wS5LhaY9uBW4Bfl9V1y5q41V1dpILgf/XUVZJ9gR+BFzcJojnAXvQXMtP92hqcV8XSZIkSZIkjcFICYF5VXVNkhuBorkxtE/7+ZfAh4F3TG6IktTTZu0L4F8005m9C5jZUWcb4AvAx4G7gdNp/oYNtduO6GrzM8CKNFMKPRb4J73n6R9tvYnyDZrk7PY0i7uvOLwhyeFVtecEHONzNNPC3aeqTk6yOc25fYzmGl4I7FxVv+7RxuK+LpIkSZIkSRqDsa4h8FeaKYQKWJ5mpIAkLTZVNYtmHZPR1L0UeHGPTT3/9lXV3TSJzg+P0O6o6rV1FxhrVU0fqTzJ+jSLCn8HOAe4g+Zv76OBtwGvBEadEKiqIXpcv6o6YQHl5wNbjLLtUV+XTitMmcKJO71+LLtIkiRJkiRpHEZKCCyX5FHAyjQ3it4B7Nt+/hKODpCkyXYV8ElgB+DlwCo0SYEbgJ8DB/UvNEmSJEmSJD2UjJQQ+DvNdBvLAj8DqKpzAZLcUlU/m9zwJGnpVlXzgf3blyRJkiRJkjRuC00IVNWrO7+n8Q2aEQJPTvKNqtptMgOUJEmSJEmSJEmLbsQ1BJKsUFV3JgnwQeBKmjUEPgBMmeT4JEmSJEmSJEnSBFhoQiDJFOAUYPOqqiT/U1WbLZ7QJElLgzvnz2ebH/yg32EsVie+8pX9DkGSJEmSJC2FllnYxnbu6kryzST/bzHFJEmSJEmSJEmSJthCEwKtAN8D9qSZKkiSHhKSTE9S7Wt+khuTnJVk1ySj+fs32uNUkqMWsG1WkqGJOtZESbJxkpPaazI3ydlJntNVp7pedyS5OMmrerS3bJIPJflzkjuTXJ5k18V3RpIkSZIkSRrJAm+IJXlMkm8Dd1fVKVW182KMS5Im0nnAbsDBwGOBbwDfa9dGWeok2RS4EHg88AngM8B/AKcnWbOr+hXAzu3rvTSJ4e8l2bqr3reAzwI/B95Fs97MN5K8Z7LOQ5IkSZIkSWOzsCdkbwW+DZBkapJXLJ6QJGnCXV1V36qq/wY2As4AdgLe0N+w+mbf9n1GVR1WVZ8BXgmsAuzdVXd2VR3dvr4MbA7cBOw/XCHJNsDrgCOqaueq+iqwPfAL4JNJVpnc05EkSZIkSdJoLDAhUFXzquo0moWHfwSssdiikqRJUlV3Ae9vv+7Wz1j6aDpwO3BLR9n5wCE0T/YvUFX9G7gKeEpH8Zvb98931CvgBJokwwOmIpIkSZIkSVJ/jGYO7aqqTdsnPh+TZPskz0qy7GQHJ0mToaouBW4EnpVGz3n+k/ymuzzJbkn+2DGf/nMXJZYkmyY5LcmtSeYkOTnJ0zu2P7Fd/+ArPfb9RZLr2/n7V02yxgiv5dtdfwWsBnw2yQrtNbmrqvauqiNHiHc54InADR3Fm9KMJPhLV/ULadagmTfGyyJJkiRJkqRJsNCEQDu/9uc7ir5BM93GW4BfJ/nIJMYmSZNpFs3T66uNdockewBfp5lSbR+aG94/HW8ASbYChoAVgA/SzOW/LnBhkqcAVNU1wGnAa5Ks1LHvk2mevP96Vd1D83T/9SO8ntfuvg/NSIB9gFlJPpVk6gLCXK6dNm5qe8yvAasDX2jjeDgwDbiue8eqOqeqXlNVPxvH5ZEkSZIkSdIEW+hT/u2UD6d0fP+f4c9JVgTek+QRVXXr5IUoSZNi+Kn1lUdTOckUmnnzbwBeWFW3t+W30tzMH5M24Xoo8Dua+fvvbTf9gmZh3ncAe7VlXwW2BHYEjm7Ldmn3+Vr7/YCObQtyKUBVXZ9kY2BP4J00awq8J8kHq+rwrn2eB/yzq+yAjnoPb9/HNAogye7A7gBTp01ju2WXrkFnQ0ND/Q5BCzF37lx/Iw0U+6QGjX1Sg8Y+qUFjn9SgsU+q07jvwFTVHcBnJzAWSVqcVmzf546y/vo0T8J/ZTgZ0DqScSQEgPWAddrPs3ts36jj84nA32nWPDg6yTLAzsCp7QgCqupy4PLRHryq5gGfT/IFmoTEQcBhSVaqqgM7ql4GvK/9vDrwGmCfJKmqfbg/kfGg/0+SrEazXsE1VXVT1/FnAjMBpq+7bp1wzz2jDX2JcOKMGf0OQQsxNDTEDH8jDRD7pAaNfVKDxj6pQWOf1KCxT6rT0vVIpiTd7wnAv3ngwrrdHgHc3H5+VPt+fVedv47z+NPa96OA/+ux/b6RV1V1T5JvAB9NsjZNImEt7h9BMGpJVgbWBv5RVf+oqvnAd5OcSzOCYL8kB7flADdV1RkdTRyT5DTgA0m+B/wWmN9xPp22A74JvAr4wVhjlSRJkiRJ0sQazaLCkrRESfJUmqfdf9FOjdarziNoFs8dNpw46L7x/ZhxhjGnfZ9XVWd0vtptd3XVPxIoYFea6YL+RjNyYKw2prmJ/4HOwqr6O3AuzRRKC1pPYNhl7ft6VXUX8Htg7XZEQKfh6zfepIkkSZIkSZImkAkBSUuVJMsBw1PifLl9nwus2VX1gzzwb+QfaEYLvDzJCh3lu44zlCuBvwDbJVm1I741gYuAj3VW7lhc+M3ADjSLCc9n7K4A7gFelmT5juM+DHg28A8evGYAHfWmAJu1X69t348GpgDv6qi3HM1URDcDl4wjTkmSJEmSJE0wpwyStDRYJ8kbgccDrwM2BA6pqh+1288G9kzybpqpbV5Fc6P/vumB2ml7Pg18HhhKcgzNOgBvHk9AVVVJ9gR+BFycZCbNwrx70IwO+HSP3b4K/JBmip4jx3ncG5McRJPw+FmS/6X5v2AXmuvztqq6t2OXxyR5A01yZHVge5rEwUXABW2dQ9ryTyRZg2YEws4013nvds0ZSZIkSZIk9ZkJAUlLg82ATYF/Ab8EPlRVndPt7EuzRsAngE8C5wAvAI7tbKSqDkxyG80iuwfRPOW/PXDqeIKqqpOTbN4e92PA3cCFwM5V9eseu5xIkzQ4s6quG88xWx+mebr/7TSjJW6hmfbnv6rq9K66GwDfbj/fAswCPgocNpw4qKq7kmxBcx1fD7yF5tq8saq+jSRJkiRJkgaCCQFJS6yqmgVkFPVuobmR3W3jHnWPAI7oKl7gMapq+gjHPh/YYmF1kjwHeAawLrAScNjC6o+kXTfhy9w/ZdKC6o147Trq3kaTaPjwosQmSZIkSZKkyWNCQJIG3xrAwcBtwL5VNa4RCYNqhSlTOPGVr+x3GJIkSZIkSUs8EwKSNOCq6gRgxX7HIUmSJEmSpIe2ZfodgCRJkiRJkiRJmnwmBCRJkiRJkiRJWgo4ZZAkqa/unD+flx/7k36HsUT78U7b9jsESZIkSZI0ABwhIEmSJEmSJEnSUsCEgKSlXpLpSap9zU9yY5KzkuyaZML+TrbtH7WAbbOSDE3UsSZKko2TnNRek7lJzk7ynK461fW6I8nFSV7Vr7glSZIkSZL0YCYEJOl+5wG7AQcDjwW+AXwvSfoaVZ8k2RS4EHg88AngM8B/AKcnWbOr+hXAzu3rvUDRXLutF1/EkiRJkiRJWhjXEJCk+11dVd8CSHIAcBKwE/AG4Nv9DKxP9m3fZ1TVTQBJLgDOBPYG9umoO7uqjh7+kuTbwDXA/jTXUZIkSZIkSX3mCAFJ6qGq7gLe337drZ+x9NF04Hbglo6y84FDgCsXtmNV/Ru4CnjKZAUnSZIkSZKksTEhIEkLUFWXAjcCz0qj5zz/SX7TXZ5ktyR/7JhP/7mLEkuSTZOcluTWJHOSnJzk6R3bn9iuf/CVHvv+Isn1SZZNsmqSNUZ4Ld/u+itgNeCzSVZor8ldVbV3VR05QrzLAU8EbliU85YkSZIkSdLEMSEgSQs3C1iF5sb4qCTZA/g6cCvNtDoXAj8dbwBJtgKGgBWAD9LM5b8ucGGSpwBU1TXAacBrkqzUse+TgecAX6+qe2ie7r9+hNfz2t33oRkJsA8wK8mnkkxdQJjLJZnavp4MfA1YHfjCeM9bkiRJkiRJE8s1BCRp4ea17yuPpnKSKTTz5t8AvLCqbm/Lb6W5mT8m7YLGhwK/A14J3Ntu+gXwc+AdwF5t2VeBLYEdgeH5/Hdp9/la+/2Ajm0LcilAVV2fZGNgT+CdNGsKvCfJB6vq8K59ngf8s6vsgB71hs9rd2B3gKnTpvHyKff2qqYJMjQ01O8QHlLmzp3rNdNAsU9q0NgnNWjskxo09kkNGvukOpkQkKSFW7F9nzvK+usD04CvDCcDWkcyjoQAsB6wTvt5do/tG3V8PhH4O82aB0cnWQbYGTi1HUFAVV0OXD7ag1fVPODzSb5Ak5A4CDgsyUpVdWBH1cuA97WfVwdeA+yTJFW1D12qaiYwE2D6uuvWj+c7YG0y/XjGjH6H8JAyNDTEDK+ZBoh9UoPGPqlBY5/UoLFPatDYJ9XJhIAkLdwTgH/zwIV1uz0CuLn9/Kj2/fquOn8d5/Gnte9HAf/XY/utwx+q6p4k3wA+mmRtmkTCWtw/gmDUkqwMrA38o6r+UVXzge8mOZdmBMF+SQ5uywFuqqozOpo4JslpwAeSfK+qLh5rDJIkSZIkSZpYPpIpSQuQ5Kk0T7v/oqpqAXUeQbN47rDhxMG0rqqPGWcYc9r3eVV1Ruer3XZXV/0jgQJ2pZku6G80IwfGamPgt8AHOgur6u/AuTRTKC1oPYFhl7Xv643j+JIkSZIkSZpgJgQkqYckywHDU+J8uX2fC6zZVfWDPPBv6R9oRgu8PMkKHeW7jjOUK4G/ANslWbUjvjWBi4CPdVbuWFz4zcAONIsJz2fsrgDuAV6WZPmO4z4MeDbwDx68ZgAd9aYAm7Vfrx3H8SVJkiRJkjTBnDJIku63TpI3Ao8HXgdsCBxSVT9qt58N7Jnk3cAPgFfR3Oi/b3qgdtqeTwOfB4aSHEPzhPybxxNQVVWSPYEfARcnmUmz0PEeNKMDPt1jt68CPwTm04wYGM9xb0xyEE3C42dJ/pfm/4xdaK7P26qqcyXgxyR5A01yZHVge5rEwUXABeOJQZIkSZIkSRPLhIAk3W8zYFPgX8AvgQ9VVed0O/vSrBHwCeCTwDnAC4BjOxupqgOT3EazyO5BNE/5bw+cOp6gqurkJJu3x/0YcDdwIbBzVf26xy4n0iQNzqyq68ZzzNaHaZ7ufzvNaIlbgN8D/1VVp3fV3QD4dvv5FmAW8FHgsK7EgSRJkiRJkvrEhICkpV5VzQIyinq3AK/vsWnjHnWPAI7oKl7gMapq+gjHPh/YYmF1kjwHeAawLrAScNjC6o+kXTfhy9w/ZdKC6o147SRJkiRJktR/JgQkacmxBnAwcBuwb1WNa0TC4rbClCn8eKdt+x2GJEmSJEnSEs+EgCQtIarqBGDFfschSZIkSZKkwbRMvwOQJEmSJEmSJEmTz4SAJEmSJEmSJElLAacMkiT11Z3z72W7Y0/vdxjSfbadcgdffAj0yRN2Wug645IkSZIkPYgjBCRJkiRJkiRJWgqYEJAkSZIkSZIkaSlgQkBaAiSZnqTa1/wkNyY5K8muSSbs33nb/lEL2DYrydBEHWtRdV2TXq9ZHXUHLfYFXufFLcnGSU5q+9TcJGcneU7H9q3aeD/eY9+Pt9u2WrxRS5IkSZIkqRfXEJCWLOcBXwfWAl4PfAPYKsmrqqr6Gtni909g547v3wauAD7dfp+72CN6iEmyKXAWzXX7BPAI4H3A6UnWr6q/V9XJSX4BvDvJwVX173bfhwPvBn5eVSf36RQkSZIkSZLUwYSAtGS5uqq+BZDkAOAkYCfgDTQ3xJcaVXUbcPTw9yTfBmZX1dEL3ktd9m3fZ1TVTQBJLgDOBPYG9mm3fww4A9gT+Gxb9k7gUe02SZIkSZIkDQCnDJKWUFV1F/D+9utu/YxFD1nTgduBWzrKzgcOAa4cLqiqM4FzgPcmWTnJw4D3AkNVddbiC1eSJEmSJEkLY0JAWoJV1aXAjcCz0ug5V36S33SXJ9ktyR+T3JHk4iTPXZRYkmya5LQktyaZk+TkJE/v2P7Edv2Dr/TY9xdJrk+ybJJVk6wxwmv5cca4ZpLjktySZHaSLyVZrmP7qK7fcL2R2mvrjuo6j3T9OupVkqOSTEnyniSXJvl5x/axXL9fAasBn02yAjSJpqrau6qO7Dr0vsBUYA/g7cA07h9hIEmSJEmSpAHglEHSkm8W8EyaG7ujkmQP4Ms0N4T3ATYAfjreANpFZX8E/Bz4ILASzU3jC5M8s6r+UFXXJDkNeE2S91TVvHbfJwPPAT5dVfckOQTYZYRDbg4MjTHM1Wiecv8NzTm/AdgL+Atw8BjbGlV7o73Oo7l+Xbss09Z/Ac20UVd0bBvL9dsHeHb7/sYkRwKHVNWc7h2q6vwkp9KMSrkXOKWqfragAyTZHdgdYOq0aWw75Y4RQpIWn1Vz70OiTw4NDfU7BC0mc+fO9ffWQLFPatDYJzVo7JMaNPZJdTIhIC355rXvK4+mcpIpwP7ADcALq+r2tvxWmpvRY5IkwKHA74BX0twsBvgFzQ3ud9DcKAf4KrAlsCP3z/+/S7vP19rvB3RsW5BLxxonsBEws6re1sZ9LM01eDHjSwgstL3RXucxXr9hO7b1nzg893+HUV+/qro+ycY0awO8k+aJ//ck+WBVHd5jv48Bv2w/v3xhB6iqmcBMgOnrrlc/mb/iCCFJi8+2U+7godAnT5gxo98haDEZGhpihr+3Boh9UoPGPqlBY5/UoLFPqpMJAWnJN3xXa+4o669PM93LV4ZvUreOZBwJAWA9YJ328+we2zfq+Hwi8HeaNQ+OTrIMsDNwalVdA1BVlwOXjyOOkdwJfGT4S1XdmOR6YJVJam+013ks12/YMsCreiQDxnz92pEan0/yBZqExEHAYUlWqqoDu+pelOTa9vOvRnsMSZIkSZIkLR4mBKQl3xOAf/PAhWG7PQK4uf38qPb9+q46fx3n8ae170cB/9dj+63DH9opgb4BfDTJ2jQ3wtfiwU/AT4arqurGrrJ7e9Z8sM7rN9r2RnudR339OvxkOIEyXklWBtYG/lFV/6iq+cB3k5xLM4JgvyQHt+WdalGOK0mSJEmSpMljQkBagiV5KrA6cFpVVTP7zIPqPAJ4InBtWzScOJjWVfUx4wxjeL75eVV1RtexN+5R/0iaJ+t3BaYDf6MZOTDZHjQv/mj0uH6jbW+013ms1w/g9gWUj8XGwPnAgcAHhgur6u9tUmAHmkWEe41akCRJkiRJ0gBapt8BSJocSZajuZkLzcK10EwbtGZX1Q/ywL8Ff6B52v3lSVboKN91nKFcSbOQ7nZJVu2Ib03gIpp55+/TPtl+GvBmmpvOX+/xFHq/jOb6jdZor/OYrt8EugK4B3hZkuU7jvswmoWG/wH8c5KOLUmSJEmSpElgQkBasqyT5I1JPgJcArwMOKSqftRuPxtYL8m7k6yZZG+aG9D3TVtTVfcAn6aZamgoyV5JvkTHU+JjUVVFsyjtNODiJPsk2Qs4A7irPVa3r9LceF+RZsTAoBjx+o3WaK/zOK/fImunOzoI2BD4WRvfe2gWMn488LGqGu2USpIkSZIkSRoAJgSkJctmwDeB9wDXANtW1d4d2/cFjgE+QfOE+ouAF9A87X2fdrHYd9DchD6orbf9eIOqqpOBzWmedP8YsH8b3wuq6tc9djkRmAf8tKquG+9xJ8Gort9ojfY6j+P6TZQPA+8EVqAZbfJh4Cbgv6pq5iQeV5IkSZIkSZPANQSkJUBVzQIevEDAg+vdAry+x6aNe9Q9Ajiiq3iBx6iq6SMc+3xgi4XVSfIc4BnAusBKwGELqz8WVTXm2LvLR3v9RtteWzaq6zya69fWG7EfjFY7OuHL3D/l1Gj2mT5Rx5ckSZIkSdLEMiEgaZCsARwM3AbsW1Wn9jkeLQYrTFmGE3YaMdchLTZDQ0OcMGNGv8OQJEmSJGnCmRCQNDCq6gSadQMkSZIkSZIkTTDXEJAkSZIkSZIkaSlgQkCSJEmSJEmSpKWAUwZJkvrqzvn3sv1x5/c7DOk+20yZyyH2yZ5+uOPz+x2CJEmSJGkROEJAkiRJkiRJkqSlgAkBSUukJNOTVNfrtiTnJXlxv+MblmRakm8mmZNkXpLzk2y2kPpDSWYtxhAlSZIkSZK0hHDKIElLuvOAme3nqcA7gFOSbFJVvxlPg0n2Bm6uqqMWJbAkqwBnA08EPg/c0sZ3RpLnVdXFi9K+JEmSJEmS1MmEgKQl3dVVdfTwlyTHA7OAfYGdxtnm3m0bRy1aaOwNbAi8pqq+18Z3AnBVG9/2i9i+JEmSJEmSdB+nDJK0VKmqa4E5wFP6HQvweuBm4Njhgqq6Bvgj8Nw+xSRJkiRJkqQllAkBSUuVJKsBjwZu6Crfsl1f4OZ2Pv9TkzyjY/t+w2sR0Ezx88KOtQlmjSOOVYENgN9W1fyuzdsB/zXWNtt2107ynSQ3Jbk9yZlJntdV56ok5/fY97AkdyZ5dEfZWkm+lWR2krlJfplkux77bpDkuCTXt8f9bZI3j+ccJEmSJEmSNDmcMkjSkm6FJFPbz2sCn2s/f3G4QpIXAScCvwE+DCwP7Emz1sD6VXUzcDzNVD4AB9OMMvh0+33uOOKa3r7fl5joiPPm9jUmSdYFfgEsBxwC3ArsAQwl2a6qTm6rHg18PMlaVXVdu+8U4JXAyVV1Y1u2FnARcBtwYNveK4EfJnlTVX2rrbcycBYQmus6B3gJcGSSm6rq+LGeiyRJkiRJkiZeqqrfMUjShEsyHfhLj00F7FVVh3fU3Qt4EfD2qprdlm0FnARs3XEjfbj+LGBWVc1YhPg2Bc4Hvl1Vb0yyLHD3AwKtSo/9hoDpVTW9x7aTgJcCm1TVJW3ZI4HLgXvb/e5O8iSa5MY+VfX5tt5/AacCOw7fwE/yDeA1wLOAf7SHmQJcAtxYVU9v6z0T+BWwd1Ud0hHPl4CfDa+P0BXr7sDuAFOnTXvm52Z+Y8RrJi0uqzKfW5jS7zAG0pNWW6XfISyV5s6dyyqreO01OOyTGjT2SQ0a+6QGjX1y6bT55ptfXFXP6i53hICkJd1pwOdpnl5/HLAbcEiSZarqUID2/dAkU5NsTXMD/HXt/qtPUlzD0wQt2/F9i/bzBxjjlEFJVmz3OWc4GQBQVTclOQr4ELAJ8POq+nOSnwOvpbk2tJ9vohkpMWxLYCXg9z0OuUaSKe10R3+kGRWwR5I5NMmBK6vqXQuKt6pmAjMBpq+7Xp04f7WxnK40qbaZcjP2yd5+OOP5/Q5hqTQ0NMSMGTP6HYZ0H/ukBo19UoPGPqlBY59UJxMCkpZ011fVGcNfkhwNXAZ8Icn3q2p2kg2ArwKbAfOA3wK/BJ48iXHNad9XB6hmuNYZbYzjmXv/0TR/06/rse3a9n2NjrJvA0ckWR+YBWwPfKeq7uqoM41mNMA+CztwVc1N8jJgf5rruDLwryTfAz5UVbeO/XQkSZIkSZI00UwISFqqVNU9SS4HngKsDcwGTgBWBV4A/KKq5id5FvCGSQzlapo5+TduRyvc27HtceNo71/APcDje2wbLpvdUfY9mnUGXgtcSnP+3+7abw6wYmdCBSDJOjSJjCm0Ix2q6lfA1kkCPIlmYeT/oVmP4S3jOB9JkiRJkiRNsGX6HYAkLU7tAribtF+vS/JoYH3gzKo6v00GBHjrQpqZQ3MDfdzaBMD3aZ7s36UjvicAzx1He/NoRhi8MMlGHe2tCryJZvHiizrqD08P9Fqa6ZH+XFU/72r2FOApSYav1/Diwz8CfkyTgCDJ25L8Nckzq3FVVR1IM9Li2WM9F0mSJEmSJE0ORwhIWtKtk+QNNE+zr0lzA/wJwHFV9TeAJH8FXpHkA8BdwKtp1hGAZg79bj8B9ktyAM2CvesAN1XVwWOMbV+aRYC/0k7dcz3wTsb/t3lv4OfAUJJDaEYg7EHzNP8OXdMBQTMi4Ic0UyPt16O9jwMvA85KcijN1ELbAU8D9ugY1XAO8CXgO0mOpEmY/D9go7ZckiRJkiRJA8CEgKQl3WbtC5ppda4C3kW7oG1rG+ALNDfA7wZOp5k3f6jddkRXm58BVqSZUuixwD+BT4w1sHb9gufSLOz7DuBemmTDKcBe42jvj0me3ca3N810PRcCu1bV+T12ORm4kWaUwtE92ruube+/aUZMPIwmAfLqqvp+R70rkrwQ+HB73EcBf2v3+9RYz0OSJEmSJEmTw4SApCVSVc0CMsq6lwIv7rGp59/Iqrqb5ub3h8cbX0dbf6OZsqfbuxZQf8YI7f2ZZoTDaI59FzB1hDrXAm8cRVsXAK8YzXElSZIkSZLUHyYEJEl9tcKUZfjhjs/vdxjSfYaGhvjhDPukJEmSJGnJ46LCkiRJkiRJkiQtBUwISJIkSZIkSZK0FDAhIEmSJEmSJEnSUsA1BCRJfXXn/HvZ6bhf9zuMpcqxO/5nv0OQJEmSJEl94AgBSZIkSZIkSZKWAiYEJEmSJEmSJElaCpgQkLRIkjwsyQFJ/prkjiQXJ9m233EBJBlKMquPx399kkrylsV4zJWTHJzk7+3vcUmS7RbX8SVJkiRJkjS4TAhIGrckywI/Bd4H/BB4DzAX+FGSHReh3b2TvGlCguyvG9r3KxbHwZIsA/wE2BM4muZ3uRs4flCSNJIkSZIkSeofEwKSFsXbgRcAH66qvarqCOAlwHXAgYvQ7t7AmxY5uv6b375fu5iO9zpgc2Dfqtqnqg4HXgTcBHxyMcUgSZIkSZKkAWVCQNKieDNwB3DocEFV3Q2cBExPsm6/AhswcxbTcV7fvn99uKCq5gK/ADZKstJiikOSJEmSJEkDyISApHFJsjKwEXBpVc3r2nwW8D06/sYk2TLJeUluTjInyalJntGxfb92vv0Cngi8cPh7r3UAkmyS5JQk/27bOz3J8xYQ6yOSfL2t968kRyd5RFedDZMc326/Jck5STbr0dasJEPt5zcluSDJdb2OW1VDVZWqur3nRWza+H/ttfhney6/TPLyBdUfwf8Drquq7gTEO4D/pJk+aPi4ayX5VpLZSea2x92uR3wbJDk2yQ1tfL9O8soe9e5bryHJtknOTnJTklXHeS6SJEmSJEmaYKmqfscg6SEoydOBy4Bjq+pBN4i76r4IOA34Dc3T68vTzHP/CGD9qro5yX8A/9HucjDNU/Wfbr/PraoTutr7Kc0c/YcBd9FMX7Qu8JKqOqetNwSsB1wDzAO+D2wNbAt8qare3dZ7BnAecBXwDZqpfnYFntG2d3bHsWcBs4A/tHVOBv5aVe8axWXrvi5PAH7fnsfhwG3ATsCLgedW1UVjaGtV4Gbgoqp6dlv2SGBKR7V/VdW9SdYCLmqP9xXgVuCV7XHfVFXfavdfneY3LuALbb0daaaFekFVnd9x/CFgOnAEze92GnA9sGePhBFJdgd2B5g6bdoz/2fmUaM9VU2AdVZ7WL9DGGhz585llVVW6XcY0n3skxo09kkNGvukBo19UoPGPrl02nzzzS+uqmd1ly/bj2AkLREe3r4/6GZvDxvSLHb79qqaDZDkTzRTCz0POLmqLqO5+UyS/wZmV9XRC2jvKzQ3szepqn+0+5xIc0N/D+Ccjrprtu1u3d4M/wbNGgcv7qhzEM3N7m07zuenwOXAe4GzeaBNgMcDT66qRVkfYBNgFeD9VfWj9jy+BxwAjPXJ+uH/2e/oKPst8LiO72vTJDP2p0nGvAj4R7vtBOAS4P3At9qyDYELaJInZ7Xx/R/NtXo5cF9CoLUGzULG/9n+ngtUVTOBmQDT112vTp6/xihOURPl2Bn/2e8QBtrQ0BAzZszodxjSfeyTGjT2SQ0a+6QGjX1Sg8Y+qU4mBCSN173t+4P+jrRPlq8J/KmqbquqQ4FDk0xNsjXwLJoFcAFWH8tBk6xH89T/kcPJAICq+nOSZWmeZu/2gaq6t613d5IrgSe07a1EszDyFHov/rtRj7KHAW9YxGQAwK9obuB/KMkU4JKq+gvNaIexGl7AuPP3eA2wIvBGYOeO8i2BlWhGJ3RbI8mUqprfjow4O8lKSV4APJMmERB6/24rAHuNlAyQJEmSJElSf5gQkDRe/2zfp/XY9g7gE8CzgYuSbAB8FdiM5gn83wK/BJ48juMO34j+W/eGqprfXUYz3dDvusru7fj8KJpkwE9ppsXpdnePsouq6oJRxLpQVXVNklcAHwGOAVZIcj3wTWC/doHm/8/encfbOtb/H3+9zSIpDqJBEkWFip9InQZN+JYmlUqjJqJ5Ls1zEhp8G1TSKPqKVGSLiiTzkMQRZTrGDsf8+f1x3etYrfY5Z+999j5rc17Px2M91lr3/bmv+3OvfXV8v/fnvq5rrK6lXde8G/W9KX2SPHUgdgZtNMC7FtRgt87Cl4GX0NaDOB/4HW1Ux2iupE3LJEmSJEmSpGnIgoCkibqQNmf9pkmW6j2B33lw935p934YbQqcJwJ/rKo7kjwOeNkEztsrRKwzuCPJh4E1q+pNfZuvXkh719I9XV9VRw+09wjumhqp33wXCR6vqvo18OtuhMDDaU/zv49WOPn4ONq5Jcm5wMOT3Leqru3bPfhbzQZWGOV616MVFJam/Saf6/J5FfDj3loASV7O6OaWC9NIkiRJkiRNW0sNOwFJd0/djd+DgdXpu7HfLW77bOC8qrosyWrAhsAxVXVCVwwI8LoFND+b+cyhX1XnA38Dnt9NTdQ7731p8/2Pa3L0qrqJtqDwzG6R3157K9HWIvjqeNobjyQfS3JpkjW7KXrOrqp3A9fTRleM1w9oN/P37DvHyrSFlPsdBTwiyeZ9cUsDPwf+D7i927wV8K+q+k5fMWBnYKUJ5CZJkiRJkqQhc4SApEXxQeDpwAFJNqKNCHgjbUqaNwBU1dVJLgWek+SdwK3ATrR1BKDNZT/ocGCvJJ+lLey7HnBtVe3d7X8DbYqfk5Ps17X5atrc/h+YwHW8jVYU6LU3m/Zk/Gq0p+OnygjwfuCIJAcB/6bN738f/nsh47H4IvBC4INdseRMWv73G4j7EPAs4LdJ9qUtNPxc4JHAG/tGe5wO7JxkH9p6A08Bnk+bmmi0v5skSZIkSZKmMUcISJqwqroGeDzwLeCVwBeAG4HtqurQvtDtaWsGfIi2tsAltJvLd3T7Bn0S+DRt7vpv0EYT3Nh33t8CTwDO69r7APAvYMvBaXDGeB2nAlsCfwDeDnyGtnbA06vqiPG2N47zHkMbTXEdbZqg/YCNgbf3FT/G095cYCbwNVph4LPAZcBeA3GX0EYgHEr7bfemFXF2qqqv9YW+BfgesDPwJeD+wDNov/uTkow2nZIkSZIkSZKmKUcISFokVTWbtojwmxYQczowuLAtzOffoG4x3fd2r/m1eTLt5vSCcps51u3dwsM7Lqi9Lm7dhcWMR1UdRZvCZ7Lau47R/x4fGYj7B20UxILaumY+MRuPEjtzPHn2W37ppfjp88c105MkSZIkSZImwBECkiRJkiRJkiQtASwISJIkSZIkSZK0BLAgIEmSJEmSJEnSEsA1BCRJQ3XLHcVOP7tg2GlI8zxjqVv4atcnf/S89YecjSRJkiRJk8cRApIkSZIkSZIkLQEsCEiSJEmSJEmStASwICBpWkiybpJKcuCwc5mf6Zxfkpldfv2v65MclWSzSWh/2l67JEmSJEmSxsaCgCR1kuyV5LnDzmMRHQq8vHt9Cng0cGyStaf6xPeQ30+SJEmSJOkey4KAJN3lw8Bzh53EIjqjqg7qXp8GXgTcB3jbYjj3PeH3kyRJkiRJuseyICBJ92x/6d4fMdQsJEmSJEmSNHQWBCTdLSXZIcnvk9yU5LIkP0ry4FHidk5ySpI5SS5PckiS9fr2H9ibc7/btEvfHPwj8zn3I5L8Osm/k1ya5EMTya9v3YS9kqyY5CNJzkvyo0X7df7D+t375ePNbywm8vtJkiRJkiRpOJYZdgKSNF5JdgW+Dvwc2ANYHXgL8Kckj6yqq7q4XYADgaOBtwKrAm8HfpHk0VV1e9fO0V3T3wOOBw7ovl8xyukfDBwHHEabr//NwEeSnFlVh44nvz73Ak4AHgT8EjhpIr9Lr60kq3efHwrsC9wCfKUXMIH8FmS8v58kSZIkSZKGJFW18ChJmmJJ1gUuAr5TVa9cQNy9gUtpN577454C/Ah4R1V9oYv9GLAB8IqquqXb9iZgf2DjqjpnoO1a0Pn7noJ/X1V9qtv2aOB0YP+q2m2c+fWu+SbgGOBFVXXz/K59QZLMBI4dZdetXbs/7+LGnN9A+wv8bcYa0xe7K7ArwOozZjz2Mwd8b2GHSIvNfbiF61kegPVWXX7I2UgwZ84cVl555WGnIc1jn9R0Y5/UdGOf1HRjn1wyPfnJTz6lqh43uN0RApLubh4PrAJsB4z2JPsmvQ9V9UGAJOsk2QLYAnh5t3sN4Jz/PnyhLgP6b5if1b33/ss65vz6/Bt42USLAQO+B3wXWBp4CPBG4AdJXl5Vh0wwv0lXVQfQjSRYd/0N6ld3PnRxnFYak2cs9Xd6ffJHM9dfSLQ09UZGRpg5c+aw05DmsU9qurFParqxT2q6sU+qnwUBSXc3M7r3T9Oeqh90Ze9DksfTpsrZFLgBOA34E7DjIpz/L1V1a+9LVd2ZZEL59fluVd2wCDn1u7CqelP4kOQg4GLgm0kOnWB+kiRJkiRJugewICDp7mZ2935t/41vgCT/D7it+7wycDhtHvtNgTOqqpK8gEUrCMwe4/4F5jfgpkXIZ4Gqak6Si4DH0kZFTCQ/SZIkSZIk3QMsNewEJGmc/kCbYuclSZbtbeymBDoReG23aUNgNeDQqjq9KwYsC7xqAW1fDdxnMeW3WCRZC3g4rehw9RTnNxm/nyRJkiRJkqaIIwQkTTcbJdlzlO23VtVXqurfSd4BfB04Kcl3gGWBPWjT3ezTxf8duBF4TZIrgBWAXWiLDAOsOMo5DgdemuSDtIV3NwJOq6rvjzX5ceQ3VR6d5GXdOR8MvAJYCfhCVd0G3DaF+S3y7ydJkiRJkqSpY0FA0nSzefcadCNtPQCq6oAklwPvAT4FzAFGgPdV1T+6mOuS7NDt/wztqfjDu2MOB7YHfjlwjj2AW4A30ebavxT43XgvYCz5TaEdu1fRFg0+D9iLttjwVOc3Kb+fJEmSJEmSpoYFAUnTQlXNArKwuL74/wP+byExxwJbjrJr1PN0C/u+oXuNtn9+x/3X9jHmN2t+uYxXVY2Mp62x5DcQv9C2F/b7SZIkSZIkabgsCEiShmr5pcOPnrf+sNOQ5hkZuZQfzbRPSpIkSZLueVxUWJIkSZIkSZKkJYAFAUmSJEmSJEmSlgAWBCRJkiRJkiRJWgK4hoAkaahuu6N436H/HHYa0jwb5zb75D3cJ3dcZ9gpSJIkSdJQOEJAkiRJkiRJkqQlgAUBaZIkWTdJDbwuT/KbJM9ejHnMSjIyhrhKcuDUZzS5+n7nA4edy2RIsmmSI5JcnWROkmOTbLmA+JEksxZjipIkSZIkSbqHcMogafKdCOwPLAs8BHgdcESS11XVN4aa2X96OfD3YSexJEuyNfBb4Dzgw8AqwNuB3yTZsKr+Ncz8JEmSJEmSdM9iQUCafBdX1UG9L0m+Sbvh+w5g2hQE+nPU0Hyge59ZVdcCJDkROAbYE3jXkPKSJEmSJEnSPZBTBklTrKouBs4G1h1yKpp+1gVuAq7v23YCsA9w/jASkiRJkiRJ0j2XBQFpiiVZGlgbuHxg+zOTHJ/kuiSzk/wqyWZ9+09Lckr3eZ1u3vyTu+8P7L7vtQh5zXce/t46BEnWTnJIkuuTXJHky0mWHYh9RZKzktzYvb8myc+S3Jxk5sD1npDk2u6aR5I8YaL5j1WSJ3Xnuqmbp/97SR4wVdeb5D5J1lrIa7muuT8DqwKfSrI8QFXdWlV7TnR6qSQbd/lc013HcUm2GW9cknsl+XeS/xpJkuQX3foYy4y1PUmSJEmSJA2fBQFp8i2XZPXu9UjgQOD+wPd6AUmeAvwCWBF4L/AxYD3gqCSrdmEnAQ9PEmCTbtvGSZYCNu6+nziF17EqcBxwJ23qmvOB3YHd+q5jB+A7wEXAHsAptGmRrgdeC1zQxW1Ou94VgPfTrnlF2vU+cKouIMkLaNPvrNWd96vAc4E/JXnQQPiqTM717gNctpDXVl2TvfO8C5iV5GNJVl+E692M1m/WA/YC3gesBIwkefJ44qrqJuAQ4H+SrNh37P2ApwM/qKrbx3NeSZIkSZIkDVeqatg5SPcISdal3SgeNBf4GvDuqrqti90deArwhqq6otv2bOAIYLuqOjLJq4BvAQ8FXgQ8AdgWeDSwPfA5YLXe3PN9ecwCZlXVzIXkW8B3quqVo+ybBTwYOKCqXt9tW402yuFXVbV9t20/4M3AKlX1727b1cCJVbVdX3tvBz4PbFpVp3fbHgK8G/h6VZ26oFwHcluX9juPmntf3IrAP4AbgUdW1Zxu+1a0aXl+WlUvmuzrTbIRbUTIgpzSt2bAirSiw5u7HG6k9ZX953NdI8C6VbXuKPt+Czwc+H+0fgdtoeJzgN9U1Q7jjHsqcDTwoqr6SbdtV+DrwGOr6i/jaW8g112BXQFWnzHjsV864PsL/MGkxWlFbmYuKww7DU2hdVZdduFB08icOXNYeeWVh52GNI99UtONfVLTjX1S0419csn05Cc/+ZSqetzgdhcVlibfCPAJYHXgYOCXVfW2/oCq2hfYtxtFsB3wOOCl3e41uveTuveNaUWAPwMP7D5vDPxtsBgwyW6hPendy/nqJJcB/f8F+R3tRvZrkvwU2Bq4L/C3gbZ+DxRtapz9gVOr6iLgDVOY/1a0v8GXe8UAgKr6Q5KTgO2TLF1Vd3S7JuV6q+oc2o3wMamqucDnknwReCHwBWC/JCtW1efH2k5XWHgisDStEDJok/HEdY4FLgVeAvyk2/YS4Oy+YsB42punqg4ADgBYb/0N6uzaaAFXJy1eG+cc7JP3bDvPXGfYKYzLyMgIM2fOHHYa0jz2SU039klNN/ZJTTf2SfWzICBNviuq6miAJM8Hnp/kkVV1Vi8gycNpT1lvQ3ui+kzgT8AGfe2cS5uKplcQ+ClttECvIDCV0wUBXFBVVw9su3Pg+zG0KXA+D+zdbTsa+HB/UFWdmGRn4K3AYcAySS4C9q+qL0x24p21uvdLRtn3D2BL4D7ANd22SbvesUiyEvAQ4MqqurIrTPwwye+A04G9kuzdV7BYmPvRbsr/EvjiKPtvG2ccVXVnku8Deya5D20aoCfSVzgZT3uSJEmSJEkaLtcQkKbWR7v3Dw5sP4x28/+JtOlntqTNPT9Ptfm8TgYeA2wInNG9NgE24q4RBFNl9hhivkkbEbEWbUqj9apq26q6fjCwqn5QVVvQbio/hjZf/+eTvGzyUv4PV3TvDxhl3wOAm2kFl55Jvd4x2JRWCHpn/8aq+hdtJMJKtBEOY3UtcEfXxtH9L+Cf3DWVz1jjer4HLA/sCOzUbeuf32e87UmSJEmSJGlILAhIU6iqzgQOBV7QzS3fm5t+Q+CYqjqhqu7oFg5+3ShNnAg8nzadzd9pBYHtaNPYTPUIgbHYFLipqmZX1e+7aYD+S5IDk5yfZJmqurVbM+Ct3e4tpii3P9Ce/n9N9zR+L5ctaaMDjhzH0/c9mzKG6x2j84DbgWclWa4vv3vRfpMrgavG2li3CPDxwMz+BZO7az+OtqDymOP62j0b+AttSquXACNVdel4zytJkiRJkqThc8ogaep9lPZ09QeAl3Zz018KPCfJO4FbaU9e9xb5WLHv2JNohbuzqqqSnNF9n0srDgzb74BdkvSmPbqB9tT92VXVP5/8ccAuwK+S/Iw2jUzvafNjJ3jujZLsOcr2W6vqK1V1U5I30dZxODnJAcBqwB60G+1vn8A5x3q9C9X1gy/QFlb+fZLv0v5N3oU2guH1VTU4ZdHCvI12c/7kbgHk2cAraNf9qgnE9XyPu6ZIeuUinFeSJEmSJElDZEFAmmJVdXqSnwM7JflIVf0V2J423/qHaDfHfwO8izYdzfbc9VR1bxTAGV1blyWZDZxXVbcvvquYr28BL6YVPF4LrNDbkWT/qtoNoKq+neQW2oK8H+3i/grsUlWHTvDcm3evQTcCX+nO+6MkVwF7AZ+iFVIOB9473hv4nTFd7zi8l7aewRto6xJcD5wNPL2qfjPe5Krq1G4ExMdoBY+lgNO69o4Zb1yfHwCfoxWvDpnoeSVJkiRJkjRcFgSkSVJVs4DMZ9+OA99PB546SugyA3GzB9usqhkLyWPdhWcLVTVqrgtqo397kg1pi+z+gDYC4GbajeDVgNcDLwR26zv2YNrT+otkQb/zfOJ/C/x2ITHrLmz7eK93jLkVrXjxlXEcM3Mh+8+iFSwW1s6Y4rrYK4BlJ6s9SZIkSZIkDYcFAUkTdQHtaf/nAf9DW9fgZuBy2vz9XxhealNiSbvexWbZpcMnd1xn2GlI84yM/I2dZ9onJUmSJEn3PBYEJE1ItyDvR7rXPd6Sdr2SJEmSJEm651lq2AlIkiRJkiRJkqSpZ0FAkiRJkiRJkqQlgFMGSZKG6vY7iv0PvWLYaUjzrJHb7ZPTwJt3XHPYKUiSJEnSPY4jBCRJkiRJkiRJWgJYEJAkSZIkSZIkaQlgQUATkmTTJEckuTrJnCTHJtlyIGYkyawhpTipkqyR5IAk/0hyc5Izk+yygPi9klSSdRdjmkOR5HlJ/pxkbpJ/JvlMkuXnEzutf5ckByapIefQ+41mDjMPSZIkSZIk3fNYENC4JdkaOAl4APBh4JPAo4HfJFl7mLn1S/LcJHtNQjv3A04Gngt8HXgncCdwYJL/WdT2786SvA44BLgV2BP4CfAO4MdDTGtRfB14+bCTkCRJkiRJkqaCiwprIj7Qvc+sqmsBkpwIHEO7KfyuIeU16LnALsBei9jO64AHAdtX1REASQ4CLqL9Fv+3iO3fLSVZE9gbOJ3WF27tts8F3pNkZlWNDDHFcauqPwJ/HHYekiRJkiRJ0lRwhIAmYl3gJuD6vm0nAPsA5w8joSm2bvf+r96GrhDyUeC3w0homngxsBKwT68Y0Dm0e3/a4k9JkiRJkiRJ0vxYENBE/BlYFfhUb674qrq1qvasqm8MBidZJck3k8xOck2Sg5KsMhDzkCQ/SHJtkpuSHJNkq1HamrcuQZIdurULrk1yn27bzG7+9aKNDqD3vX/u+iQrJllrIa+V+64X4NNJ1ujlUlVfrKr3TOQHTHK/JPslubS73tOTvDZJxhvX/Vb/GOXYd3TX/KjxnneMtu7eB5+ovwj4EXDxeBtMsk6Sb/fld3aSNy4g7pJu7YLzk7x7lN9gTHF98QtcQ6Dry3sP5LdnkikdbdX1108kuTBtDYtzk7wnybIDcfPWaEjyyCQ/SnL54NRWSe6T5EtJLup+l1lpaz+sMN7zpv1v+44k6wwc+4wul92m4jeRJEmSJEnS+DllkCbiXcAW3fsrknyD9pT47FFilwWOAuYC7we2A3YGrgb2AEiyPu2m8rK0UQY3AG8ERpI8t6qOHGw0ybuBTwC/Bn5Gm8Me4FzumgN+V2Ab/nNO+Ku6952Aby/kOj9Cm27o28AOwHOAWUl+BHy+qs5eyPGjSiuG/B64H7A/cBnwdOB/gXW68445Dvhel+PWtJEaPS8BTquqM8fZ3lg9tHu/pH9jVV1FGz0wLmnFpWOAtYHPd/k9FfhKkuur6uC+8COBhwBfBP4BPBb4NHBbt228cWPJbyXgd8AjaWsNnA7MpE2btBGtv026rtjwS1ru+wF/BzYHPgVsCLxqlMNmAl8BzgaOoF17v+8Cz6blfh7wMODtwIrAW8Z53gOAV9MKcJ/sO8cutJFE35vPde1K95vNmDGDNXLmgn8IaTFahrn2yWlgZOTcYacwbcyZM4eRkZFhpyHNY5/UdGOf1HRjn9R0Y59UPwsCGrequizJpsBuwJtp8+i/Ncm7q2r/gfC1gTOA7arqziTfot1AfmpfzD7AfYHNq+pUgC7uHOB/k6xbVbf1xa9Fu3n5mKo6YyC3K4CDujaeBmxTVQeNchm/ArZdyKVe2LV5J/DcJC8C3ga8Etglyf8Ce1TVzQtpZ9CetBuqTwHO6rYdSrtx/dYkH6+qO8YRdwjtBv9L6AoCSTYAHkP7ncZ73rG6d/c+dxzHLMj6tJvTe1XVDwG6YtOTaetBHNxtW422iPWXqmqv7thvJbkKmPe3GGvcOOwBbALsWlX/2207IMkDgVcneWtV3TiBdhfmpcCTaIWto7pthwEzaAW5d1fVlQPH7NvlOVrfh9YHzeL05QAAXutJREFUflFV89b7SPIv2sifcZ23qk5Kcjrtfxef7NpahfY3+0FV9U8tNk9VHUArJvDQ9TeoK+tRo4VJQ7FGzsQ+OXwvnLnmsFOYNkZGRpg5c+aw05DmsU9qurFParqxT2q6sU+qnwUBTUhVzQU+l+SLwAuBLwD7JVmxqj4/EP7O7qY6VXVbkvNpi/TSTVHydOC4XjGgi7s2yYHAe2hPJf+hr73lgd0HiwHjzP8y2hPo4znmx8CPkzye9nT5rrQn67cf5+mfCQQ4dj77H0wrRowprqr+neQw4IVJ9qiq22nFgTvobqKP87xjdWf3vgx3jdDoPVn+SODqqrpktANH0424eG6SZZNsSStoPB1Yo3v14q5Oci6wU5KzgZOAc/tu+o8rbhy2o13nNwe2PwlYqvvdp8Izu/dRn7Sn/daDa1kctIBiALTC0dOS7NF9Pquq9l2E8x4A7J/kiVX1O+BFtNEGX19ADpIkSZIkSVrMLAhoXLppUx4CXNk9HXwH8MMkv6NNobJXkr37njSfU1VnDTRzZ9/n1Wj9cLQbx71pTtYa2H4l8ONFuY7xSPJI4Maqugigqv6YZBva9DbbJfl/VXXSOJqcQVugeJf57L9ynHHQbtq+lDby4le0gsBvquryCZx3rK6ijTiYAfyzb/sDgFNpU9a8eayNdfPSfwZ4PbACrTjxe+DyUcKfQ5sy6nO0p9rnJPk/4B1dsWe8cWOxBnBZr7jV032/c/RD5i/J+4EPAk/vbqIP6rU5o/v8jPk0NVphbPDm/qBX0p7mfx/tum5J8mta8e6vEzjvQcBnaVMH/Y7Wx06rqj8tJA9JkiRJkiQtRhYENF6b0p4o/jzwzt7GqvpXVxR4HrA6cEW36+qFtHcNcDvtJvKg3rYrBrbPrar5Lvw6BU4FTqONVACgqm5P8lPgicC6tKfPx2o23dPV/TeXu0VZH0h7in88cQC/od04f0mSK2k36j86wfOO1V+AJ9Ce5O8vCDy4e790nO29A3gr8F7gK1V1Q5ffYEGJqvob7Sl0uil7nkGbempN4GnjjRujq4DHJFlq4Pd7Km1anS9X1V/G0d4ttNEuqwxsv1/33ptqZzZtAfgz+qcGSrI6bZql0dy0oBN3U2u9pmtnTdqaA/sAv6CtJzCu81bVDd3aGjsl+RKtX/zXYtCSJEmSJEkarqWGnYDuds6j3cB/VpLlehuT3Iu20PCV3LVw70J1Uw8dDTwpySZ97d2H9hTz5cDJE8x1dl9bi+JM4JFJ1hvY/uTufbyLCx8FrAw8f2D7/wLHc1ehbqxxdCMyDgZ2pD2l/W/afO8TOe9Yfb973yNJfzHhpd374DQ2C7MVUMAX+ooBT6IVN+ZJ8uwklyZ5LkBVXVJV36AVRbYYb9w4HEG7gf/qge2v5K4FdMej94T9NgPbnwDcSPvfGtw1f//gyI6P0hbjvv94Tprk0d3vshu04kBV/Qj4IbB+kl5BYrznPQBYiTZ6Zw539Q9JkiRJkiRNE44Q0Lh087J/AXg38Psk36X1o11oT/S/fnBKlTHYk7ZGwEiSfYAbaE8XrwE8r6puXcCxC/IL2qK6B3bTxNwP2KCqXj/Odj4I/B9wQpL9aaMankGbjuYHo0yJtDB7Ay8Gvt9NPXQWbR76ZwGfqaprxxnX8z3aose7AQdW1eAN6vG2t0BV9aduDYm3AUclOYQ2YmJn4LBxTqMEbcqp7YFvJzmOdtN+F1qRYMW+uJOAewFfTfIo2kiEjWhz/B85gbix2oe2XsZXk2zW5bs18DLgO1V13oIOHsXRwJ+Bt3XFtb8CzwY2Az5RVbd0cd+nFSE+k2Qj2s34TWhTK/2oqs4c53nPpS2q/Mkk9wcuoI3qeA1tNMA1Ezlv3+LCmwD/W1X/HmdekiRJkiRJmmIWBDQR76XN7/8G2tRB19Oekn96Vf1mvI1V1V+TbEGb03xPYDnazdxXVdUJE02yqkaSvIK2MPHXgLlMYO2BqjoiyTOA93dtAfyNNj/+uBdN7aZX2Rr4GO1p/dfRbsq+qb+9scb1xZ/WTa/zSEZZCHa87Y3xWt7eLdz7Ftq89VfR5uwfnK5oLD5Gmz7nhcALaCMzdqY9gf+UJA+qqn90RamtgQ/QFnaeQRuZ8rVuWy+3McWN41rnJHlid20vBF7FovWDO7vphj4MPJdWULuYVsTauy/u9iTP7HJ+afe6mFao+sIEzntbkpl97d0fuBb4GW1NgUU5709oBQEXE5YkSZIkSZqGsninYpck3dMkeRttZM87gWuq6vHjOf6h629Qb/vc8VOSmzQRa+RMrqxHDTuNJd6bd1xz2ClMGyMjI8ycOXPYaUjz2Cc13dgnNd3YJzXd2CeXTElOqarHDW53hIAkaVHtRJvq6C/AK8Z78DJLxxt/mlZGRs7lhTPtk5IkSZKkex4LApKkRVJV/2/YOUiSJEmSJGnhlhp2ApIkSZIkSZIkaepZEJAkSZIkSZIkaQnglEGSpKG6447iR4fMHnYaS5ydnr/6sFOQJEmSJEmLmSMEJEmSJEmSJElaAlgQkDQhSdZNUgOvG5Mcn+Sp0yi/vaao/Upy4GTFjaGdmUlGklzfvQ5PsuGitruQcx6YpKbyHJIkSZIkSVp8LAhIWlTHAy/vXu8H1gSOSrLpMJO6J0nyQuC33df3APsCTwWOTnKvoSUmSZIkSZKkuxXXEJC0qC6sqoN6X5L8DJgFfAB4wbCSuof5KHAZ8PSquhUgyYXAN4FXAl8ZXmqSJEmSJEm6u3CEgKRJVVX/AGYDjxh2Lvcg6wKze8WAzpHAPsDlQ8lIkiRJkiRJdzsWBCRNqiSrAqsxcKM6yTO79QWuSzI7ya+SbDbK8c9MckKSa7vYkSRPGCVu6yS/TnJD196RSR41SkrLJvlSkmuS/DvJj5OsM0p7Oyc5JcmcJJcnOSTJehP/JSbVn4FHJ9k9yVIAVXV5Ve1ZVT/rD0yySZJfdNd6Q5LDkmw02GCSDZL8pPudr0/yhyTbLyyRJPsluSPJKwa2P7z7zS5LclOSM5O8ZhGvW5IkSZIkSZPIgoCkRbV8ktW716OBg7vtX+oFJHkK8AtgReC9wMeA9WhrDazaF7d5F7cCbT2C93bHHJXkgX1xzwZGgOWBdwOfBNYHTkoyODJhN+CZwEeArwM7AsclWbmvvV2Ag4BrgLcCXwC2Bn6RZDpMrfYGWoHly8AFSd7Zn39Pkq2APwKPAT4OfBp4PO13eUxf3MbAn4CnAHvT1iVYGjg8yc7zSyLJF4E3Aq+qqu/2bV+JtsbBVrS/++7AWcA3kjxv4pctSZIkSZKkyTQdbnRJunt7cffqKWD3qjq8b9vGwOHAG6rqCoAkfwOOoN1EPrKLeyLtxvRrqur0Lu4o2k3/1YFLkoS2qO5ZwAuBO7tj/wj8AXgT7YZ0T4Btquqqrr1/Al+kzb2/XxezPvBj4BVVdUsXdyOwP7ABcM4EfpdJU1Vnd0/5vwN4LfBZ4N1JXl9Vh/SFHgDcBmxeVf8ESPJD2m/1NWCLLm5v4F7AplV1Thf3XdpUT7sD3x/MIcmngD0YKAZ0Hg7cH9izqvbptn0zyVXAsqNdU5JdgV0BZsyYwXJLnzbGX0OTZWTE/xNgfubMmcPIyMiw05DmsU9qurFParqxT2q6sU9qurFPqp93AyQtql8Dn6PdeF8HeDWwT5KlqmpfgO59324UwXbA44CXdsev0dfW72kFhU8l2R84taouoj0h3/Mw2ugCgCtGyWeTge8/7xUDOgfTCgLb0BUEquqDAEnWSbIF7cb5y/vyG2pBAKCqrgXen+SjwKtoT///NMmLquonSR5KK7x8q1cM6I67MMnPgRcnWRu4ljYy4NheMaCLu3G0UQcASfaijSKYCxw3SshfacWENyaZTZvi6PyqessCrucAWgGD9dffoG69Y9Mx/hKaLDNnrj7sFKatkZERZs6cOew0pHnsk5pu7JOabuyTmm7sk5pu7JPq55RBkhbVZVV1dFX9pqoOpN1sPh/4YpI1Yd788scBV9KexH8Wbcqa/1BVJwI700YDHAb8M8mFSd7eFzajez8Q2HaU1zsGmr104PuVtFEF9+ttSPL4JKd2sQfSRi38V37DkGTVJI/sTa1UVbdU1ddoBY1bgI92oWt175eM0sw/+mLuRxuF8c/BoKq6o6ruGOX4DwJvA+bQFjIePG4O7W/6d9q0TOcBs5N8JckqY7lOSZIkSZIkTT0LApImVVXdTnuifhngId3mw2hT7zwRWKWqtmSUG8vd8T+oqi2AlWhz4R8HfD7Jy7qQ2d373K4QMe/V7bt1oMm1Br6vRvu373qA7qn4w4HlgE2BVavqSdy1FsKwbQecSSuUzFNVZwJnA+t2m3qjJR4wShsP6Iu5BriDNprjPyR5c5IDk9xrYNerq2pv4APAc7o1HP5DVf25qrYD7k0bxfEp4PW00RiSJEmSJEmaBiwISJpU3QKzm3dfL0myGrAhcExVnVBVd3TrALxulGMPTHJ+kmWq6taqOpW2yC/cNf/9+cBFwHOT3Kfv2LWBk2lPs/d7TpL79n1/Yff+x+59Q1qR4NCqOr2qKsmytGl5poMzu/f/6d+Y5P603M8GqKoLaE/mv6Db14tbF3gu8Jeq+mdVzQWOBZ7UrUvQi1uONgrgGbSpgeapqu90H78BnA58OckKfce+PsmlSR5bzQVV9fku9y2QJEmSJEnStOAaApIW1Xrd0/tLA2sDLwEeBBzSt7DtpbQb8++kPcG/E20dAYAV+9o6DtgF+FWSn9EWyN2p23csQHfDfjfg58ApSQ6g3cB+Y9f2JwbyWxE4PsnXaU/K70mbVueb3f6/AzcCr0lyBbBCl8MGo+S32FXVGUl+BOyU5DfAz4BVaE/fr8h/FkB2pa3pcHKSfWlF3z1o6zL0r8OwJ229ht8l+TJtZMWLaWszvKqqaj653JlkT9rf4t3AR7pdxwFfBn6Q5Btde/+Ptp7Dlxfl+iVJkiRJkjR5HCEgaVFtA3yPNvf+O2g359/Cf05xsz1tTv4PAR+m3ZB/Cm3qmu17QVX17e64FWhz4+8N3BfYpaoO7Ys7EngybaTAB2k3pi8GnlhVfxnIb9/u3B8D3gwc0cVd17V1HbBDd/xnums4EXheX+7D9nLab/dg2lRLewDnAltW1S97QVV1PG39gzNov8v7aNe+ZVWd3Bd3Nu3J/RHaqIBP0IoG23brQMxXVY3QihLvSbJet+084EldTnsCXwGeBnwceOciXLckSZIkSZImkSMEJE1IVc0CMsbY04GnjrLrv/4NqqqDGcP8/VV1Am0R4bHm9+oFxB4LbDnKrvleX1WN9drHFLeQNm6jFTQ+NobYU4H/muN/lLjzgRcsJOaVwCtH2f78UbadCDxnYeeVJEmSJEnS8FgQkCQN1dJLh52ev/qw05AkSZIkSbrHc8ogSZIkSZIkSZKWABYEJEmSJEmSJElaAlgQkCRJkiRJkiRpCeAaApKkobrjjuKXP5o97DSkeW5b7nb7pKaVYfbJZ+3kGi+SJEnSPYkjBCRJkiRJkiRJWgJYEJA0LSRZN0kNvG5McnySp06j/PaaovYryYGTFTeGdlZLckCSK5PMTfK7JFstaruSJEmSJEmavpwySNJ0czxwQPd5deBNwFFJNq+q04aW1T1IkvvQfueHAF8ELgdeAxyb5ElVdeIw85MkSZIkSdLUsCAgabq5sKoO6n1J8jNgFvAB4AXDSuoe5oPAI4CdqurHAEm+D1wCfBqYObzUJEmSJEmSNFWcMkjStFZV/wBm025gaxElWRrYBbi4VwwAqKprgOOArZOsMKz8JEmSJEmSNHUsCEia1pKsCqxGm9amf/szu/UFrksyO8mvkmw2yvHPTHJCkmu72JEkTxglbuskv05yQ9fekUkeNUpKyyb5UpJrkvw7yY+TrDNKezsnOSXJnCSXJzkkyXoT/yUmzYa0qZhGmxbocOAQYMXehoVdR5KZ3boG70jyky7umiTfTnK/Kb8aSZIkSZIkjZkFAUnTzfJJVu9ejwYO7rZ/qReQ5CnAL2g3rt8LfAxYj7bWwKp9cZt3cSsA7+9iV+ziHtgX92xgBFgeeDfwSWB94KQkgyMTdgOeCXwE+DqwI3BckpX72tsFOAi4Bngr8AVga+AXSYY9VdtDu/dLBndU1Ver6sVVdS2M+zo+BqxD+/1+TBuFcPQ0uF5JkiRJkiR1vFEjabp5cffqKWD3qjq8b9vGtKfZ31BVVwAk+RtwBLAVcGQX90RgaeA1VXV6F3cU7ab16sAlSQLsC5wFvBC4szv2j8AfaIsa79537gDbVNVVXXv/pC3M+0pgvy5mfdpN8VdU1S1d3I3A/sAGwDkT+F0my72797ljiB3PdVwKzKyqW7u4fwPvAJ5DG3XwH5LsCuwKMGPGDG5b7rSJXIs0JSpz7ZOaVobZJ0dG/H8X9N/mzJnDyMjIsNOQ5rFParqxT2q6sU+qn/8XvqTp5tfA52g33tcBXg3sk2SpqtoXoHvftxtFsB3wOOCl3fFr9LX1e1pB4VNJ9gdOraqLgDf0xTyMNroA4IpR8tlk4PvPe8WAzsG0gsA2dAWBqvogQJJ1kmwBbAG8vC+/YRYEegWP//r3P8kDaIWSs6vqtnFex/d7xYDOj2gFgS0ZpSBQVQcABwCsv/4Gteytmy7KNUmT6rblTsM+qelkmH1y5szVh3JeTW8jIyPMnDlz2GlI89gnNd3YJzXd2CfVz4KApOnmsqo6uvclyUHAGcAXk/y4qq5I8nDadD3b0J50PxP4E+2p9Xmq6sQkO9OmuzkMWCbJRcD+VfWFLmxG934g8P1R8rlh4PulA9+vpN1knzdffpLHA18BNu2OP63Lb8cFX/pi0StmzBhl38dpU/2sAVw1zuu4bD7nWXWRspUkSZIkSdKkcQ0BSdNaVd1OexJ9GeAh3ebDaDf/nwisUlVbAvvM5/gfVNUWwErAY4DjgM8neVkXMrt7n1tVR/e/un23DjS51sD31Wj/ll4P0K0lcDiwHO1G+qpV9STuWgth2E6jFTAeM8q+B9MKLNdM4DoGF1Zes3u/ZhHzlSRJkiRJ0iSxICBpWkuyErB59/WSJKsBGwLHVNUJVXVHtw7A60Y59sAk5ydZpqpurapTaaMFoE1/A3A+cBHw3CT36Tt2beBk4IMDzT4nyX37vr+we/9j974hrUhwaFWdXlWVZFngVRO4/ElXVVcDRwGPSfLE3vYkD6atv3BcVd3B+K/jZUmW7/v+ku79hEm/CEmSJEmSJE2IUwZJmm7W657eXxpYm3Zj+UHAIVX1T4Akl9JuzL+T9gT/TrR1BABW7GvrONoUOL9K8jPgti4W4FiA7kb3bsDPgVOSHEB7Sv6NXdufGMhvReD4JF8HHgDsCVwCfLPb/3fgRuA1Sa4AVuhy2KDv+GF7C63I8n9JvkDLt1co+XD3Pt7rWB0Y6aZ4ejStQPNH7lrgWZIkSZIkSUPmCAFJ0802wPdoc/q/g3Zz/i3Azn0x29Pmsv8Q7Qb2JcBTgDu6fQBU1be741YAPgrsDdwX2KWqDu2LOxJ4Mm2kwAeBjwAXA0+sqr8M5Ldvd+6PAW8Gjujiruvaug7YoTv+M901nAg8ry/3oaqqv9MKAkfQCgGfAC6gXcefupjrGN91fJD2+30GeBHwLeBZ3WgDSZIkSZIkTQOOEJA0LVTVLCBjjD0deOoou/7r37SqOpgxzN9fVScA244jv1cvIPZYYMtRds33+qpqrNc+prgxtHMx/1lkGS1mPNdxfVW9dDJykyRJkiRJ0tSwICBJGqqllw7P2mn1YachzTMysgwzZ9onNX3YJyVJkiRNFgsCkqQJq6oRxjiyQ5IkSZIkScPlGgKSJEmSJEmSJC0BLAhIkiRJkiRJkrQEcMogSdJQ3XlHcfz3rhp2GtI8c1a63T6paWWy++Q2L58xaW1JkiRJuntxhIAkSZIkSZIkSUsACwKSJEmSJEmSJC0BLAhI0iRKsm6SGnjdmOT4JE+dRvndkGTVgX2zktSQUpMkSZIkSdIUsyAgSVPjeODl3ev9wJrAUUk2HWZSfe4NvHEyG0zyyiR7TmabkiRJkiRJmjwWBCRpalxYVQd1ry8BTwOWBj4w3LT+w1uSLD+J7b0S2HMS25MkSZIkSdIksiAgSYtBVf0DmA08Yti5dG4C1gJeMexEJEmSJEmStHhYEJCkxaCbr3814PKB7Zsk+UWSf3fz+h+WZKO+/Yclmd19XjrJ3CRXdt+XSXJLkgMnkNLhwJXA25PM978FSTZO8rMk1yS5PslxSbbp2//K3loJwJOAB/evnzCBvCRJkiRJkjRFLAhI0tRYPsnq3evRwMHd9i/1ApJsBfwReAzwceDTwOOBk5I8pgs7CVgtyZrAhsAKwIwk9wfWB5YDTpxAfjcD+3RtPme0gCSbdedfD9gLeB+wEjCS5Mld2O+4a62E82ijIF7e95IkSZIkSdI0kSof4JSkyZJkXeCiUXYVsHtV7d8XexbwQGCjqvpnt2094CzgrKraorvx/lvgqcAawLuBVYE3ACsDPwU2q6rTxpnfd4A9gH8AZ1fVVklmAQ+uqnSxvwUeDvw/YG7XxCrAOcBvqmqHgbZHgHWrat0x5LErsCvAjBkzHvutrx28kCOkxeeOpeay9J0rDjsNaZ7J7pMr32+ZSWtLS6Y5c+aw8sorDzsNaR77pKYb+6SmG/vkkunJT37yKVX1uMHt/n8DkjQ1fg18DgiwDvBqYJ8kS1XVvkkeCmwMfKtXDACoqguT/Bx4cZK1gZOBO7vY+wOnA/cFHg2sSFsL4MyJJFhV1yf5GvCu/mmAAJKsCDyRthDyP0Y5fJOJnLPv3AcABwA8bP0N6j43LlJz0qS6fqXTsU9qOpnsPrnN82ZMWltaMo2MjDBz5sxhpyHNY5/UdGOf1HRjn1Q/CwKSNDUuq6qje1+SHAScAXwxyY9pC/oCXDLKsb0b8GtV1b+SnE0rCDyANlqgVxBYDvhzVd2xCHnuTRsp8K6B7fejFQN+CXxxlONuW4RzSpIkSZIkaQgsCEjSYlBVtyc5B3gE8BDgim7XA0YJ723rxZxEKwg8mLYGwX2B/6HdsD9yEfO6PMl3gNfR1hXouRa4o4s5uv+YJI8A7r0o55UkSZIkSdLi56LCkrQYJFkJ2Lz7eklVXUBbhPcF3QLBvbh1gecCf+mbSuhEYCvaegNndK9HARsxsQWFB32ONi3RvAmqq+om4HhgZpIHDVzHccBXR2lnNnDvJJmEnCRJkiRJkjTJHCEgSVNjvSQvoz3FvzbwEuBBwCF9N/p3pa01cHKSfWlF2j1oCxC/oa+tk7p9V1TVlUlm057mX7Hbt0iq6oIkhwAvGtj1NlpR4OQk+9Fu+L8CWA141ShNHQ48H/hGkuNpax7cu6ret6g5SpIkSZIkadFZEJCkqbFN9wK4BrgAeAvdQroAVXV8kq2ATwAfpBUCjgXeV1Vn9bV1DnADbWQAVXVnt67AWv0LEi+iTzNQEKiqU5NsCXwMeDutKHEa8PSqOmawgar6TpLVgTfSCgfXA/tOUn6SJEmSJElaRBYEJGkSVdUsYMxT5lTVqcCzFxJzJ3CfgW2bzyd8Qvl1eYy2/Sxgx3G0/wXgCxPJTZIkSZIkSVPLgoAkaaiWWjps8/IZw05DmmdkZBm2eZ59UtOHfVKSJEnSZHFRYUmSJEmSJEmSlgAWBCRJkiRJkiRJWgJYEJAkSZIkSZIkaQngGgKSpKG68/bi1G9cOew07pY2e+0aw05BkiRJkiTdjThCQJIkSZIkSZKkJYAFAUn/Jcm6SWrgdXmS3yR59mLKYVaSkTHEVZIDpz6jxWci1zSfv9mNSY5P8tSB2FmjxPZePxxP3HToK5IkSZIkSRobpwyStCAnAvsDywIPAV4HHJHkdVX1jaFmdpeXA38fdhLTyPHAAd3n1YE3AUcl2byqTuuLmw28dZTjZw18H2vc3aGvSJIkSZIkLdEsCEhakIur6qDelyTfBM4D3gFMi5u8/fkJgAsH/mY/o928/wDwgr64G8f42401btr3FUmSJEmSpCWdUwZJGrOquhg4G1h3yKlojKrqH7Sn/B+xmM9rX5EkSZIkSZpmLAhIGrMkSwNrA5f3bXtmN0/9dUlmJ/lVks369p+W5JTu8zrdHPMnd98f2H3faxFymu98+711CJKsneSQJNcnuSLJl5MsOxD7iiRndfPun5XkNUl+luTmJDMHrveEJNd21zyS5Al9+++TZK2FvJbri391kr925zklyePncy1bJ/l1khu63/nIJI8aw++zKrAafX+zxWG0viJJkiRJkqThsiAgaUGWS7J693okcCBwf+B7AEmeAvwCWBF4L/AxYD3anPWrdm2cBDw8SYBNum0bJ1kK2Lj7fuIUXsOqwHHAncC7gPOB3YHdegFJdgC+A1wE7AGcQpvm5nrgtcAFXdzmtOtdAXg/7ZpXpF3vA7vm9gEuW8hrq669NwLfBG7ocjsJ+OXgBXSL844AywPvBj4JrA+clGTwyf/l+/5mjwYO7rZ/aSBuqb64/tfSE4xbYF+RJEmSJEnS8KWqhp2DpGkmybq0m+OD5gJfA95dVbcl2R14CvCGqrqiO/bZwBHAdlV1ZJJXAd8CHgq8CHgCsC3waGB74HPAalV17UAOs4BZVTVzIbkW8J2qeuUo+2YBDwYOqKrXd9t6T8v/qqq277btB7wZWKWq/t1tuxo4saq262vv7cDngU2r6vRu20NoN+m/XlWnJtmI9mT8gpxCKwJcBtwBPLSqbura+3TX3neq6pVdIeWCLv4ZtMIGwMOAPwD7VdXuC/ibFbB7Ve0/yu8yms16iw+PJW6sfWVwZ5JdgV0BZsyY8djvfOXgwRCNwYqruxTQVJgzZw4rr7zysNOQ5rFParqxT2q6sU9qurFParqxTy6ZnvzkJ59SVY8b3O6dBEkLMgJ8Alid9qT5L6vqbb2dVbUvsG/3VPh2wOOAl3a71+jeT+reN6YVAf4MPLD7vDHwt8FiwCS7BXhfX85XJ7kM6P8v4e9oBYHXJPkpsDVwX+BvA239nnaD/VNJ9gdOraqLgDf0tX8OcM7CkuoKBzOAr/WKAZ1v0AoCPQ+jjboAuGKUpjYZ+P5rWpElwDrAq4F9kizV/b16rgBeNkp7Fwx8H2vcCAvoK4Oq6gDgAICHPXSDWuu6R88vVAuw2QvWWHiQxm1kZISZM2cOOw1pHvukphv7pKYb+6SmG/ukphv7pPpZEJC0IFdU1dEASZ4PPD/JI6vqrG7bw4GvA9vQngg/E/gTsEFfG+fSpt7pFQR+Shst0CsITOV0QQAXVNXVA9vuHPh+DO1p/c8De3fbjgY+3B9UVScm2Rl4K3AYsEySi4D9q+oL48zrft37ZQPbLx34PqN7PxD4/ijt3DDw/bLe3wwgyUHAGcAXk/y4N5IDuLk/bgHGGrfAviJJkiRJkqThcw0BSWP10e79g33bDqPd/H8ibbqdLWlz6M9TbV6yk4HHABvSbk6fQXuyfSPuGkEwVWaPIeabtCfc16JNabReVW1bVdcPBlbVD6pqC2Al2jUdB3w+yWhP0S9Ir+0ZA9vXHPjey39uVR3d/+r23bqgk1TV7bQRC8sADxlnjhM1Wl+RJEmSJEnSkFkQkDQmVXUmcCjwgiQbdXPxbwgcU1UnVNUd3Xz3rxvl8BOB59Om7/k7rSCwHW3anqkeITAWmwI3VdXsqvp9Nw3Qf0lyYJLzkyxTVbdW1am00QIAW4zznOcC1wH/k2T5vu2vGog7nzZH/3OT3Kcvl7VphZYF3nRPshKweff1knHmOCGDfWVxnFOSJEmSJEkL55RBksbjo8COwAeq6qVJLgWek+SdtCfVd6KtIwCwYt9xJ9EKkGdVVSU5o/s+l1YcGLbfAbsk6U17dAPtCf6zq+offXHHAbsAv0ryM+A22jUDHDueE1bV7Uk+QZvvfyTJwbT1Al4zEFdJdgN+DpyS5ADa7/ZG2m/+iYGm1+tGKyxNW9z4JcCDgEOq6p99cSstYFTDD7uRBQuNW8Alzusr3LWuhCRJkiRJkobIgoCkMauq05P8HNgpyUeA7YEvAh+i3Rz/DfAu2vQ72wNf7Q7tjQI4o2vnsiSzgfP6bjwP07eAF9NuYL8WWKG3I8n+VbUbQFV9O8kttAWIP9rF/RXYpaoOHe9Jq+rzSW4E3g58gTYaYEfgVwNxRyZ5Mm1Ngw/SfuuTgJdX1V8Gmt2mewFcQ1v89y10C/j2WR343nxSOwyYM8a4UQ32lar66/xiJUmSJEmStHhYEJD0X6pqFpD57NtxYNNTRwn7j39bqmr2YHtVNTh3/uB51l1Ynl3cqHkuqI3+7Uk2pC0q/APaCICbaaMXVgNeD7wQ2K3v2IOBg8eS21hU1Ve5q3AyL61R4k4Atl1AO7NGO24+setOYtyc+Z13lL4iSZIkSZKkIbIgIGlJdwHtaf/nAf9DW9fgZuBy4A+0J/c1hZZaJmz22jWGnYYkSZIkSdI9ngUBSUu0qroD+Ej3kiRJkiRJku6xlhp2ApIkSZIkSZIkaepZEJAkSZIkSZIkaQnglEGSpKGq24u/7XfFsNO4R3vYbmsOOwVJkiRJkjQNOEJAkiRJkiRJkqQlgAUBSZIkSZIkSZKWABYEpCVIknWT1MDr8iS/SfLsxZTDrCQjY4irJAdOfUZTJ8m9ksxNcv4CYjZNckSSq5PMSXJski0XEH9xkn8nWW4R8hrsA7OT/D7Jzkkyn2NmJZk1xvYGXw+faK6SJEmSJEmaPK4hIC2ZTgT2B5YFHgK8Djgiyeuq6htDzewuLwf+PuwkFtG2wArAw5JsUFX/URhIsjXwW+A84MPAKsDbgd8k2bCq/jUQvwnwoO7rTODXi5Db34G9gKWBBwCvBA4CHgF8YALtnQd8Yj77/jWf7ZIkSZIkSVqMLAhIS6aLq+qg3pck36Td0H0HMC0KAv353Y3tMPD5CwP7ezfeZ1bVtQBJTgSOAfYE3rWQ9halIDB7oA/sR+sDeyTZq6puH2d7V9xD/maSJEmSJEn3WE4ZJImquhg4G1h3yKncY3RT72wHHAXM4j9v5vesC9wEXN+37QRgH2C0aYZ2oN20H5lPexNWVdcDvwdWBlafzLYlSZIkSZI0PVgQkESSpYG1gcv7tj0zyfFJruvmmP9Vks369p+W5JTu8zrdXPEnd98f2H3faxFymu8aAr11CJKsneSQJNcnuSLJl5MsOxD7iiRnJbmxe39Nkp8luTnJzIHrPSHJtd01jyR5Qt/++yRZayGv/nn9NwfWok0JdCywdZL7DlzKn4FVgU8lWR6gqm6tqj0Hp25KslbXZq+9Byd5VN/+lceQ34oL+dkfCNwOzF5InCRJkiRJku6GnDJIWjItl6T3FPhawLuB+wMfB0jyFOAXwGnAe4HlgN2Ao7q57a8DTgJe1j0Jv0nX1sZJlgI27r6fOIXXsCpwXJfju4CXAbsDFwF7d9exA/Cd7lq+BGxDmxLpQOC1wAVd3Obcdb3vB4o2p/5RSR5RVZfQntrfZSE5PZn29D7c9QT/sbRCy6uAZwEH98W/C9iie39Fkm8A+1TVaDfktwPStXcF8JHuHGd2+99BW4dgQV5Fu3aAZfr6wP2AXbtcDprAdEEAy/a11zO3qm4cLTjJrt05mTFjBheueeZoYZok/xw5d9gp3K3MmTOHkZGRYachzWOf1HRjn9R0Y5/UdGOf1HRjn1Q/CwLSkmnH7tUzl3YT/aPd942Bw4E3VNUVAEn+BhwBbAUcSbvZvyttUeJHd/u2BR7WHV+0osFU2QQ4oKpe3+X3U9qN96d21wLwjO79pVX1b+AbSbYH1hiY7/6JtMV1X1NVp3ftHUUrlKwOXAJ8lrbo7oKc3vd5B9pUQKdy18iLHegrCFTVZUk2pRVb3kxbU+CtSd5dVfsPtL0D7TcdAW6g/c12AD7Z7f8ubbqhBTm77/Njgav6vt8OfA9400LamJ+tBtoD+DrwhtGCq+oA4ACADR66Qa13xaNGC9MkedgL1xx2CncrIyMjzJw5c9hpSPPYJzXd2Cc13dgnNd3YJzXd2CfVz4KAtGQaAT5Bu9l9MPDLqnpbb2dV7Qvsm2T1JNsBjwNe2u1eo3vv3ezfmFYQ+DNtyplHd9v+1lsod4rcAryvL+erk1xGmwO/53e0G+2v6QoGWwP3Bf420NbvaTfbP5Vkf+DUqrqIvpvZVXUOcM5YEkvyIFrB4ujufDcDFwPPTLJM/xP4VTUX+FySLwIvpC08vF+SFavq8117K9CKLb11BVYBTgG2SrJGVV1ZVRcCF44lv865wFtooz9+DPy1ql4xjuMHnQG8fWDbpYvQniRJkiRJkiaZawhIS6YrquroqvohcAiwY5JH9nYmeXiS44AraTeLnwX8aaCNc2lPwPcKAmd0r15BYCqnCwK4oKquHth258D3Y4DLgM/TnvL/YbftP6bWqaoTgZ1pBZLDgH8muTDJ4A3usdq+e38a7an5q4AH06Y52gYgyUpJHplkjS6HO7q/x+a0Ofz36tZ2AHgKcC9gw772nkD7N3y7CeZ4Q9cHjgS+CjymK/5M1LVde/2v8xahPUmSJEmSJE0yCwKSetMEfbBv22HABrSpdFapqi1pc+jPU1UFnAw8hnajulcQ2ATYiKmdLgjGtvDtN2mjIdai3UBfr6q2rarrBwOr6gdVtQWwEu2ajgM+n+RlE8itt37Ai2hP9m9Lm+O/f9+mtPn/3zmQx79oIxtWohUo+o95S197LxvYtyg+B9wEfGgS2pIkSZIkSdI0ZUFAWsJV1ZnAocALkmyUZDXaDf5jquqEqrqjWzj4daMcfiLwfNr0PX+nFQS2o03bM9UjBMZiU+CmqppdVb/vpgH6L0kOTHJ+N53PrVV1KvDWbvcW4zlhkpVoiwufW1U/6T0tD3yFu+b9BziPNm//s5Is13f8vbpzXsldc/JvT1s34Gt9T99/H5gFbJtk+fHkOKiqrgS+BmyR5JmL0pYkSZIkSZKmLwsCkqCNEgjwgW4ankuB5yR5Z5I9aHPsv6qLXbHvuJNo/46c1Y0YOKP7Prf7PGy/A3ZJsm+SXZO8OMmzujn++x1HWwz5V0nenGRX2lRKAMeO85xPB5anLbI8T7dWwG+B9ZM8vPudv0CbXun3SXZP8lbgD8ADgA9W1Z1JNuu+/7qqbhs41xG04suTx5njaD5H+7t9cGGBkiRJkiRJunuyICCJqjod+DmwU5INaU+k/4k2hcyHafPvPwW4g7vmx4e7RgGc0bVzGW0qn1P6F84dom/RFgvekTbl0Q+AI4GLk+zXC6qqb9PWEFiBVhzZm7YY8C5Vdeg4z9kbAXDkKPuOGIh5L23R4+Vp6xy8F7gWeHpVHTCB9iasqi4Hvk5bqPhpi9qeJEmSJEmSpp9lhp2ApMWnqmbRRgKMtm/HgU1PHSXsP/7NqKrZg+1V1YyF5LDuwvLs4kbNc0Ft9G/vChvH0IoAxwE304qgqwGvB14I7NZ37MHAwWPJbSF5vxp49Xz2fZW2gG/ve9GmEvrKAtr7KHet8zC475fM5++5kBzn1wfeyl1TJQ3uW3e87Y1VlgkP223NRWlCkiRJkiRJY2BBQNI91QW0G+nPA/6HNrXOzcDltGl5vjC81CRJkiRJkqTFz4KApHukqroD+Ej3kiRJkiRJkpZ4riEgSZIkSZIkSdISwIKAJEmSJEmSJElLAKcMkiQNVd1eXPbZfw47Dd2D3f9d6ww7BUmSJEmSpgVHCEiSJEmSJEmStASwICBp2kiybpIaeF2e5DdJnr2YcpiVZGQMcZXkwKnPaGoleVOSWsD+Byb5QZKrklybZL8kK0w0TpIkSZIkScPjlEGSpqMTgf2BZYGHAK8Djkjyuqr6xlAzu8vLgb8PO4lFkeRFwL4L2L868Dvg/sCXgOuAdwH3BXYeb5wkSZIkSZKGy4KApOno4qo6qPclyTeB84B3ANOiINCf391NkqWAjwHvBi4H1p5P6IeBdYH/qarDu2NPA36Z5OtV9btxxkmSJEmSJGmInDJI0rRXVRcDZ9NuOmvRPQrYFdgR+M1oAUkCvBA4rXeTH6CqjgLOB14wnjhJkiRJkiQNnwUBSdNekqVpT7Ff3rftmUmOT3JdktlJfpVks779pyU5pfu8Tjfn/8nd9wd23/dahJzmu4ZAbx2CJGsnOSTJ9UmuSPLlJMsOxL4iyVlJbuzeX5PkZ0luTjJz4HpP6Obnv65r/wl9+++TZK2FvJbrwi8BNuq/gT+KNYA1gd+Osu/PwGPHGSdJkiRJkqQhsyAgaTpaLsnq3euRwIG0+em/B5DkKcAvgBWB99Kmv1kPOCrJql0bJwEP755g36TbtnE3Xc7G3fcTp/AaVgWOA+6kzad/PrA7sFsvIMkOwHeAi4A9gFNoUyJdD7wWuKCL25x2vSsA76dd84q0631g19w+wGULeW0FUFXXVNVVC8n/ft37haPsu4K7RmuMNU6SJEmSJElD5hoCkqajHbtXz1xgb+Cj3feNgcOBN1TVFQBJ/gYcQbvpfSTtZv+utEWJH93t2xZ4WHd80YoGU2UT4ICqen2X309pIxye2l0LwDO695dW1b+BbyTZHlhjYI2CJwJLA6+pqtO79o6irQGwOu2J/88CC1vX4PRx5N8rGM8ZZd9NwH3GGfcfkuxK+/swY/UZnPXgc8aRmjQ+fx3527ji58yZw8jIyNQkI02AfVLTjX1S0419UtONfVLTjX1S/SwISJqORoBP0G52Hwz8sqre1ttZVfsC+3YjCLYDHge8tNu9Rvfeu9m/Ma0g8Gfggd3njYG/VdW1U3gNtwDv68v56iSXASv3xfwOeDPwmq5gsDVwX2Dw7uXvaQWMTyXZHzi1qi4C3tDX/jnAZN5Vv6l7zyj7lqKNVhhP3H+oqgOAAwA2eOgG9ciLN5p4ptJC3H+ndcYVPzIywsyZM6cmGWkC7JOabuyTmm7sk5pu7JOabuyT6ueUQZKmoyuq6uiq+iFwCLBjN3UQAEkenuQ44Ergx8CzgD8NtHEubeqdXkHgjO7VKwhM5XRBABdU1dUD2+4c+H4MbSqfz9Oe8v9ht+3D/UFVdSKwM61AchjwzyQXJnn7FOTd8y9aEeIho+xbg/bbjidOkiRJkiRJQ2ZBQNJ015sm6IN92w4DNqBNpbNKVW1Jm0N/nqoq4GTgMcCG3FUQ2ATYiKmdLghg9hhivkkbDbEW8ARgvaratqr+6yZ6Vf2gqrYAVqJd03HA55O8bPJS/o/z3QKcDTxplN1b0goBY46TJEmSJEnS8FkQkDStVdWZwKHAC5JslGQ12g3+Y6rqhKq6o1s4+HWjHH4i8Hza9D1/pxUEtqNN2zPVIwTGYlPgpqqaXVW/76YB+i9JDkxyfpJlqurWqjoVeGu3e4spzO8Q4IlJHteXyxNoIyx+NYE4SZIkSZIkDZFrCEi6O/gobZHhD1TVS5NcCjwnyTuBW4GdaOsIAKzYd9xJtMLnWVVVSc7ovs+lFQeG7XfALknmAmcCN9Cm2Dm7qv7RF3ccsAvwqyQ/A26jXTPAsVOY3360hX9/3v3WS9GmN7oR+NoE4iRJkiRJkjREjhCQNO1V1enAz4GdkmwIbE9bM+BDtPn2LwGeAtzR7evpjQI4o2vnMtpUPqdU1e2LJ/sF+hZt/v0daVMe/QA4Erg4yX69oKr6Nm0NgRVoxZG9aYsP71JVh05VclU1G3gGcBXwfeB73a6dquqC8cZJkiRJkiRpuBwhIGnaqKpZQOazb8eBTU8dJew//k3rblRnYNuMheSw7sLy7OJGzXNBbfRv7wobx9CKAMcBN9OKtKsBrwdeCOzWd+zBwMFjyW08quqVwCsXsP/MJI8B/h9wb+APVTVnonGSJEmSJEkaHgsCkjQcF9Ce9n8e8D+0dQ1uBi4H/gB8YXip/aequhP442TFDcoy4f7vWmciqUmSJEmSJGkcLAhI0hBU1R3AR7qXJEmSJEmSNOVcQ0CSJEmSJEmSpCWABQFJkiRJkiRJkpYAThkkSRqqur24/AvnDTsNaZ7bH3CzfXIh1nr7w4edgiRJkiRpAhwhIEmSJEmSJEnSEsCCgCRJkiRJkiRJSwALApI0BknWTVJJDhzY/rwkdyb5XZJl+7bfK8ncJOeP0tbMrq2Lkiw9sK+SzBrYtmmSI5JcnWROkmOTbLmAXC9O8u8ky030egfa2yHJ7UnWnc/++yX5WpJ/dfl9P8n9JuPckiRJkiRJmjwWBCRpgpI8AjgQuAR4flXd1rd7W2AF4GFJNphPE+sCL1rIObYGTgIeAHwY+CTwaOA3SdYeJX4T4EHAysDMsV/NfM//ROCHwNLz2b888GtgV+BQ4EPAE4DDkvjfGEmSJEmSpGnEmzWSNAFJVqHdAF8aeG5VXTUQssN8Pg9650JO9YHufWZV7VdVnwReSLvhv+co8WM970Il2Q04Grh6AWG7AY8F9qiqN1fVF4FnA1sDL1uU80uSJEmSJGlyWRCQpHFKEuC7wIbAa6rq1FH2bwccBcxi/jfmbwI2S7LtAk63bhd3fd+2E4B9gP+ajqg713nAyALOu1BJ7gN8GngT8K0FhO4EXAV8tbehqs6mFRJeMNHzS5IkSZIkafJZEJCk8Xs/8Bzg01X1w1H2bw6sBfwWOBbYOsl9R4k7CLgFeNcCzvVnYFXgU930PFTVrVW1Z1V9oz8wyVrduXvnfXCSR/XtXznJWgt5rdiFzwUeNXiOUTwKOL6qbh8l78cu5FhJkiRJkiQtRssMOwFJupvZCnh593lkPjG9J/OPBS4HXgU8Czh4IO4y2kiD1yXZbHCkQeddwBbd+yuSfAPYp6pmjxK7HZDuvFcAH+lyObPb/w7aOgQL8irgwKq6FbhoQYFd8WAF4MJRdl8B3D/Jcl1bg8fuSlt3gBmrz+CsB8xaSFrS4jN3uVvtkwtx3sjlw05hiTJnzhxGRkaGnYY0j31S0419UtONfVLTjX1S/SwISNL4PIx2o/yBwOeSHF1VdwzE7ECb4udUWkGgt22wIADwWeDVtBv+LxncWVWXJdmUNlf/m2lrCrw1yburav9Rzlu0QsUNtKf8d6AtRAyt+HDCQq7v7IXs79cbZTZnlH030YoTqwD/VbyoqgOAAwA2eOgG9chL1x3HaaWpddYDZmGfXLC1dnr4sFNYooyMjDBz5sxhpyHNY5/UdGOf1HRjn9R0Y59UPwsCkjQ+NwLbA7sDbwBeQ3djGyDJg4BNaHPo3xe4GbgYeGaSZQan1qmqC5IcArwwyXtHO2FVzaUVH75IW1D4C8B+SVasqs93510B2Ja71hVYBTgF2CrJGlV1ZVVdyOhP80/UXFoBIqPs6xULVpjE80mSJEmSJGkRuIaAJI3PIVV1Dm3qnX8DH0ty777923fvT6MttnsV8GDaOgDbzKfNTwNLA2/v35hkpSSPTLIGQFXd0a1ZsDntqfu9kizdhT8FuBdtoePeeZ9A+3d+uwlf7QJU1Z20ERAPGWX3Gt379aPskyRJkiRJ0hBYEJCk8SmAqrqSdiN/DeB9fft76we8iPbE/ra0ufv79/1ng23tgF/Tpg7qtylt/v93DsT/C/gdsBKw+kDbb+k778sWdN5JcgqwTZLBUQJbAnOq6t9TeG5JkiRJkiSNgwUBSZq4vYFLgD2TPDjJSsCTgXOr6idVdXRVHQ18hbvm85+fT9Oe8O93HnA78Kwky/U2JrkXbaHhK2kjAaCNTLgB+FrvvFX1fWAWsG2S5RfxWufnENoIgR378nsI8HTgV1N0TkmSJEmSJE2ABQFJmqBubv8P0ObJ/zTtJvjywBGjxP0WWD/JqCtxVtWxwEkD266mrRewMfD7JLsneSvwB+ABwAer6s4km3Xff11Vtw00fQSwMq1QMRV+AJwFfDvJrkmeRysELE0rmEiSJEmSJGmasCAgSYvme8BfgJ1oCw0DHDlKXK9IsLBRAoPeC7yZVmj4fPf9WuDpVdVbzLjX5kTPO2FVdQttjYK/AF+njRhYC3htVf1+Ks4pSZIkSZKkiVlm2AlI0t1BVc0CBufJp6oKeOwYjv8q8NW+TaO1ddjg9q79r3Sv+bX9UeCj89n3y9HONR5VtRew1wL2/wN4cjdSYS3gT93ohjHJMmGtt486cEIaivNGLmetneyTkiRJkqR7HgsCkqRJ0S2OLEmSJEmSpGnKKYMkSZIkSZIkSVoCWBCQJEmSJEmSJGkJ4JRBkqShqtvv5IovnTLsNKR5bl/7JvukphX7pKYb+6SmG/ukphv7pKYb++TCrbnnQpeHvMdwhIAkSZIkSZIkSUsACwKSJEmSJEmSJC0BLAhIWuIlmZWk5vP64bDzG4YkBy7gN6mB2GPmE/fpYeUvSZIkSZKk/+YaApLUzAbeOsr2WVN50iR7AtdV1YFTeZ4J+Dpw9MC25YD9gCt6G5IsDWwB/Bg4fCD+rKlMUJIkSZIkSeNjQUCSmhur6qAhnHdPWtHhwCGce76q6o/AH/u3JfkgsCKwe9/mRwErA9+pqiMXX4aSJEmSJEkaL6cMkiQtVJKHAu8DDquqX/Tt2hq4Hfj9UBKTJEmSJEnSmFkQkKQxSvLMJMcnuS7J7CS/SrLZfOJOSHJtFzuS5Al9+/fqm4v/wcCT+ubdnzVKe5sk+UWSfye5IclhSTYaiBnp1kJYJck3u/yuSXJQklW6mJWTrLWQ14rzufz9gTuAPQa2bwXcABze5Xdtl98m4/hpJUmSJEmStBg4ZZAkNUslWX2U7ddW1R1JngL8AjgNeC9tPv3dgKOSbFhV1wEk2bwv7v1AAa/s4h5RVZcAPwMu6Nrfm7Z+wSe673P6T55kK9pc/tcBHweWpt2UPynJk6rqL33hywJHAXO7c28H7Axc3R3zDuDDC/kdXsXA9EVJdgKeAby7qv4xEL8NsApwMW0dgfsDuwInJNmsqi5gFEl27eKYMWMGZ659xWhh0lDMXfZ2+6SmFfukphv7pKYb+6SmG/ukphv75MKdOzIy7BQWm1TVsHOQpKHqnsp/8Hx2b1ZVpyXZHXgK8IaquqI77tnAEcB2vfnzk7wd+DywaVWd3m17CPBu4OtVdeoo555VVTPnk9tZwAOBjarqn9229WgL9p5VVVt020aAJ9EKAttV1Z1JlgUuAWZX1SO749ZbyM9xdlVd1nf+VYDzgGu63+K2vn3LAm8DTq2qX/dtfzhwOvC/VbXbQs7HBg99WB2/+w8XFiYtNmeufQWP+teaw05Dmsc+qenGPqnpxj6p6cY+qenGPrlwa+752GGnMOmSnFJVjxvc7ggBSWquAF42yvYLAKpqX2DfJKsn2Q54HPDSLmaNvvjf00YFfCrJ/rSb5RcBbxhvQt28/RsD3+oVA7pcLkzyc+DFSdauqn/1HfbOqrqzi7styfnAg3rHAReOM42PA2sBO/UXA3rtA58ZPKCqzktyDG1UgSRJkiRJkqYJCwKS1NxcVUfPb2f31PvXaVPkzAXOBP4EbNAfV1UnJtkZeCtwGLBMkouA/avqC+PMaa3u/ZJR9v2jL6ZXEJhTVWcNxN05znPOk+QxwJuA71bV8eM8fA5tZIMkSZIkSZKmCRcVlqSxOYx28/+JwCpVtSWwz2iBVfWDbiqflYDHAMcBn08y2giEBelN8PeAUfY9YCAG2loBkyLJUrQCyA3AO+cTs02SXye59yi7NwOunKx8JEmSJEmStOgsCEjSQiRZDdgQOKaqTugWGQ7wulFiD0xyfpJlqurWbs2At3a7txil+dnAfUY7b7cg73nAC5Lcv+8c6wLPBf7SP5XQJHsTbVqk91TVVfOJuQXYFnh7/8YkuwHrA/83RblJkiRJkiRpApwySJKalRbwBP8PgUuB5yR5J3ArsBPthjnAin2xxwG7AL9K8jPgti4W4NhR2j4c2CvJZ4FzaIv+XltVe3f7dwV+DZycZF9aIXcP2joF416XYCy6hYQ/TltIeO58fpffVNWfkvwC+HCSTWnrLTwKeHr3+SNTkZ8kSZIkSZImxoKAJDWrA9+bz77DgO2BLwIfot3k/w3wLmCk2/dVgKr6dpJbgDcDHwVWAP4K7FJVh47S9ie7mJcB9weuAj7c21lVxyfZCvgE8EFaIeBY4H2jrBcwWe7HXaMWvjufmCfTpivaCfgA8HLgmcDFtIWGP11V101RfpIkSZIkSZoACwKSlnhVte4Ywk4HnjrK9v/6d7SqDgYOHuO5bwPe273mF3Mq8OyFtDNzPNsX0tYsIGOMvQl4X/eSJEmSJEnSNGZBQJI0VFlmKdbc87HDTkOa59yREdZ8kX1S04d9UtONfVLTjX1S0419UtONfVL9XFRYkiRJkiRJkqQlgAUBSZIkSZIkSZKWABYEJEmSJEmSJElaAriGgCRpqOr2O7niy8cPOw1pntvXmmOf1LQy2CfXfMs2Q8xGkiRJ0t2ZIwQkSZIkSZIkSVoCWBCQFiDJzCTVvZ7bt/25fdtnDi3BKZbkwCQ1SW09L8mfk8xN8s8kn0my/Hxiv9v9tluNsm+k77cf7TVzqvO7p5jMv68kSZIkSZKmPwsC0tht2vd5s2ElsZh9HXj5ojaS5HXAIcCtwJ7AT4B3AD8eJXZp4Nnd1+1Hae4TXU4vB3rzJ7y1b9u5U5nfPcxC/75dUWyvJKsunpQkSZIkSZI0VVxDQBq7Tefz+R6rqv4I/HFR2kiyJrA3cDows6pu7bbPBd6TZGZVjfQd8nhgte7zDsD7BnL6TV/bTwO2AQ6rqlmLKb97jDH+fWcCHwYOBK6b2owkSZIkSZI0lRwhII3dZvP5rAV7MbASsE/vZnvn0O79aQPxO3TvXwMemWTdqU1v3PlJkiRJkiRJd0sWBKSxORZ4UJL7Jrkf8EDgt4NBSf5fkqOSzE5yXZITkjxllLhZSUa6z69McmKSSwZiRpLMGuXYwwa3J9kgyU+SXJvk+iR/SPJf0+0kuX+SbyW5MsmcJH9Jssv8LnqS5pjfunsffBL9IuBHwMUD23cAzgF+0Pd9Ko05vyQrJflskkuS3JLkjP7fr2/NiQ8k+W2Sm5J8KcmOSS7tfvcdu9iRrh+8NMnf+tp73mCCSWYk+XqSK5LcnOSkJNuNErdOkm93+c1Ncn6SdyfJaBc+v79vknV7azLQRgcAXDS/dRqS3C/Jft013pTk9CSvnd95JUmSJEmSNBwWBKSxObl734y7Rgf8uT8gyUbAMcB6wEdpU90sD/wiyUNHazTJV2lPwv+Lu55IH5ckGwN/Ap5Cm/rmPcDSwOFJdu6Luz9wErAT8G3gbcCVwIFJ3jfY7iTqXft/FDyq6qqqenFV/W9fjg8FHkErwJwIzGXqCwJjyi/JCrS/79uBX9J+v9m03+8TA22+t4u9ANgD+DzwOeBewAf74u4PfIO2ZsF7gGWBQ5I8vxeQZHVasWIX4HvAu7q4w5PsOnDeI4HnA98E3gz8Bvg0bY2F8biKu9Zk6PXLUddpSLIK8HvghcAB3fWeD/wv8KFxnleSJEmSJElTyDUEpLH5K/Bv2toB6T7/dSDmUcDRwAeq6ixoT4EDZwPPAL4yEL858ABgg6r6xyLktjftRvOmVXVOd97v0m5W7w58v4v7CG1kw9N78/An+Sbwd+AtwCcXIYcFuXf3PncMsb2b/8dW1a1J/gA8Kcm9q+rfU5PemPPbHfh/wFuqal+AJF+h3YR/b5If9MV+u6o+kWQlWr94d1X9NMlOwNp9ccsBr6mqb3XtfZ82MmEv2iLH0P5uDwX+p6oO7+K+RitI7Z3k0Kq6KslqwKOBL1XVXt2x30pyFXDz2H8OqKobgYO6c60P7Mj812nYE9iQVpA6q9t2KPAQ4K1JPl5Vdwwe1BUzdgWYMWMGZ651/XhSlKbU3GXvsE9qWhnsk+eOjAwvGQmYM2cOI/ZDTSP2SU039klNN/ZJ9bMgII3NncAptNEBAf7SbZunqn4E/CjJKkm2BR4LvKDbvcYobd4LeNmiFAOSrEi7EXtsrxjQ5XJjkpUHwrcDLuhflLeq7kiyXndNU6X3Oy0DzJujP8kywCOBq6uq93R+ryBwdvdk/J+Bp9IKKj8dcn7/0+3/ai+mqirJl4Bndvv/0O06rXu/dT7fe24HvtvX3pVJjgW2S7JyVc3p2r2wVwzo4m5Nsj9tdMm2wMFVdXWSc4GdkpxNGw1ybl9xYKo8k9Z/jp3P/gcDFw5urKoDaCMK2OChD6tHXX6fKUtQGq8z17oe+6Smk8E+ueaLthliNhKMjIwwc+bMYachzWOf1HRjn9R0Y59UP6cMksbuZNoIgU25awqheZKsneTnwDXA4bSpeU5ZUHtVdeIE8lil7/P9aNMD/XMwqKruGHgye435xN052hPck+iq7n3GwPYHAKfSpsohyX2A3h2Oc7vj3t19n8ppg8aUH7AWcFlV3T4Q94++/T2DMYPfe2aP0l4vn96dn7UYmM5oAed9DnACbXqiM4Brk3y/my5qqsygTXm17XxeV07huSVJkiRJkjQOFgSksTuZ9sT4xgysH9D5NvA02k3ZVapqM+D9C2jvpvEmkGRpYKO+TdcAdwDrjBL75m7R2Ht1m66aT9xOXdyDxpvPGP2le3/MwPYHd++Xdu/PpM2N/03+84byFcCzk0zVv1djze8KYK3ub9DvAX37x2tGkuUGtq0JFHBtX7sP4L/913mr6m9V9aKqui/wINq8/8+lrT0wVWbTilS/raqjey9aUWcOUzv6RJIkSZIkSeNgQUAau5Pn87lnK+DUqjqiqnpTw7x+Ec43h3bDuH9qr1fTbhgDUFVzaVO1PKlb1BiA7ibz22hT7fTmxj8CWD/J0wbO82bgJcB1i5DrgvTWMNgjSf/N4Zd277/t3nujAA4YuLH8S2B14PFDzu9w2iLR8/6mXfwetBv4hzN+SwOv7GtvTeDJtH7UKxgdDjw0ybP74pal/d3m0hYOJsmzk1ya5LkAVXVJVX2j27/FBHLrmd29z2/+lKOAlWmLGff7X+B4nJpOkiRJkiRp2vBGjTRGVTUryWxgqaq6MMkTB0JOB7ZM8hHgctrc70/t9q04gVMeS5v3/5NJ9qHdKP4kcAHtSfqePYHfA79L8mXaDdwXA+sBr6qq6uI+RHsK/+dJ9gVmAc+iTdPzkaq6YQI5LlRV/SnJF2kFiqOSHAI8EdiZtlDtSd1T98+iTS8zWGw5gnbTfIfuOhd7fl3ol2lrQuyb5NG0KXmeT1vD4bNVdUaSmeM8/Vzgi0keQrv2N9CKDh/qi/kwrbBzSJL9aFMF7QJsArypqnpT8pxEW5fiq0keRRvZsBGtDx05zrz6/Qa4Bfhyt1j1SsATqupF3f69af3t+0m2oS0s/CTa3/MzVXXtKG1KkiRJkiRpCBwhII3Pycx/XYCdaTev9wA+S3tqfGvgBuBZo0w1szD7AvvTbob/FXgN7Sbr2f1BVXU27QnwEdpN7U905962qg7si7usi/tx19bngPsDL57qhWer6u3A67rz7QvM7PLcqQvZmrYewlF9BYyeXwO3MYXrCIwhv95ojCfTboDvAHyJti7Da6rq3UzMlcDLaYWFTwM3A8+pqiP6znslsCVwEK0Q8DnaQsjPqar+BY6vpv2OvwV2pS1+vBNt4eFXTDA/quqvwI60QsB+tKLUyn37b+jO+7/ddewDPBp4E/C+iZ5XkiRJkiRJky//fe9NkjTVkowA61bVukNOZeg2eOjD6vg9vjXsNKR5zlzreh51+fxmyZIWv8E+ueZbthliNhKMjIwwc+bMYachzWOf1HRjn9R0Y59cMiU5paoeN7jdKYMkSUOVZZby5pamlXNHRljzRfZJTR/2SUmSJEmTxYKAJA1BVc0cdg6SJEmSJElasriGgCRJkiRJkiRJSwALApIkSZIkSZIkLQGcMkiSNFR1+x1cud+vh52GNM/ta95sn7yHWWO3pw87BUmSJEmaFhwhIEmSJEmSJEnSEsCCgCRJkiRJkiRJSwALApKGLsnMJNW9ntu3/bl922cOLcEpluTAJDVJbT0vyZ+TzE3yzySfSbL8fGK/2/22W42yb6Tvtx/tNbMvdtMkP0pyZXfeE5LsMBnXI0mSJEmSpMljQUDSdLNp3+fNhpXEYvZ14OWL2kiS1wGHALcCewI/Ad4B/HiU2KWBZ3dftx+luU90Ob0cOL7b9ta+bed27TwB+CPwOODT3flWBv4vye6Lek2SJEmSJEmaPC4qLGm62XQ+n++xquqPtJvqE5ZkTWBv4HRgZlXd2m2fC7wnycyqGuk75PHAat3nHYD3DeT0m762nwZsAxxWVbMGTr0vMAfYsqqu6uK/DZwKfCbJgVX170W5NkmSJEmSJE0ORwhImm42m89nLdiLgZWAfXrFgM6h3fvTBuJ7U/p8DXhkknXHe8Ik96cVbX7SKwYAVNVNwP8BKwKPGG+7kiRJkiRJmhoWBCRNJ8cCD0py3yT3Ax4I/HYwKMn/S3JUktlJruvmrH/KKHGzkox0n1+Z5MQklwzEjCSZNcqxhw1uT7JBkp8kuTbJ9Un+kOS/pttJcv8k3+rm1J+T5C9JdpnfRU/SGgJbd++DIw0uAn4EXDywfQfgHOAHfd/H677d+1Wj7LtX937jBNqVJEmSJEnSFHDKIEnTycnAk2kjA9Jt+zMw72Z/ko2AY4B/AR8FbgdeBfwiyaOq6u+DjSb5ahdzJPCniSSWZGPg98AdtKl5rgJeCRye5GVV9f0u7v7ASbTpePYD/g48DzgwyTpV9cmJnH8MHtq9/0fBo3ty/8UD1/JQ2pP7+wMnAnNpBYF9x3nOfwA3A09NsldVVdf+Sl17/wLOG+3AJLsCuwLMmDGDM9a8eZynlqbO3GXKPnkPs8zIyLBTWCRz5sxh5G5+DbpnsU9qurFParqxT2q6sU+qnwUBSdPJX4F/06ahSff5rwMxjwKOBj5QVWdBe8ofOBt4BvCVgfjNgQcAG1TVPxYht71pT71vWlXndOf9LjAb2B34fhf3EdrIhqf35uFP8k1aYeAtwFQVBO7dvc8dQ2xvNMCxVXVrkj8AT0py7/HM919Vc5J8BXgb8I0kXwTuA3yc9ht8qKrumM+xBwAHAGzw0PXr0VesMNbTSlPujDVvxj55z7LGC2cOO4VFMjIywsyZM4edhjSPfVLTjX1S0419UtONfVL9LAhImk7uBE7hrhECf+m2zVNVPwJ+lGSVJNsCjwVe0O1eY5Q27wW8bFGKAUlWpI1SOLZXDOhyuTHJygPh2wEX9C/KW1V3JFmPu0Y9TIXe77QMMG8NgSTLAI8Erq6q3uiBXkHg7CSr00ZhPJVWUPnpOM/7DuA6YA/g1X3brwS+NM62JEmSJEmSNIVcQ0DSdHMybYTApt3n/5Bk7SQ/B64BDgd2ohUR5tteVZ04gTxW6ft8P2Bp4J+DQVV1x8BT8GvMJ+7O+T0tP0l68/jPGNj+AOBU4D0ASe4DbNPtO7c77t3d93GvI1DNx7rz3o82kgLgveMZbSBJkiRJkqSpZ0FA0nRzMu2J9o1pT64P+jbwNOA5wCpVtRnw/gW0d9N4E0iyNLBR36ZraGsHrDNK7Ju7RYF7i+heNZ+4nbq4B403nzH6S/f+mIHtD+7eL+3enwksC3wT2LbvdQXw7CQT+u9Ct37ACsDraWsofHsi7UiSJEmSJGnqWBCQNN2cPJ/PPVsBp1bVEVXVmxrn9YtwvjnAjG5qnZ5XA2v2vlTVXOBY2jz78woFSZajzZ//DO6au/8IYP0kTxs4z5uBl9Cm15kKvTUM9kjSPzXRS7v333bvvVEAB1TV0b0X8EtgdeDxi5DDXrSiwJt6CwxLkiRJkiRp+rAgIGlaqapZtIV6r6mqC0cJOR3YMslHkrwxyS+BD3f7VpzAKY+lrTPwySTrJHkZbeHfCwbi9qSNNvhdkg8leRNtceP1aNPj9G6Af4j2NP7Pk3w6yRu6KY62AT5VVTdMIMeFqqo/AV+krQVwVJJdkxwE7AocVlUndSMfnkWb33+w2HJE9z7uaYMAkjwWeC2wX1X9ZWHxkiRJkiRJWvwsCEiajk5m/usC7Ey7eb0H8FmggK2BG4BndTe9x2NfYH/glcBfgdfQbpqf3R9UVWcDWwAjtFEBn+jOvW1VHdgXd1kX9+Ourc8B9wdeXFV7jTO3camqtwOv6863LzCzy3OnLmRr2jz/R43yBP+vgduYQEGgG5GwL3A58IGJ5C5JkiRJkqSpt8zCQyRpalXVCJC+78/u+3wgcGDf94tp6wcMus8o7a47hnPfCuzWvfo9d5TY84EXjKHNy4BXLSyuL/6VtILEIquqbwDfmM++39H3Ow/suwFYbiL5dcWFrcaZ6jxZZmnW2O3pEz1cmnTLjIywxgtnDjsNSZIkSZImnSMEJEmSJEmSJElaAlgQkCRJkiRJkiRpCWBBQJIkSZIkSZKkJYBrCEiShqpuv4Mr9z9s2GlI89y+BvZJTSv2SU039klNN9O9T67x5ucOOwVJkuZxhIAkSZIkSZIkSUsACwKSJEmSJEmSJC0BLAhI0hgkWTdJDbxuTHJ8kqcOOz+AUfK7Kcnvkzx7is87K8nIVJ5DkiRJkiRJi841BCRpfI4HDug+rw68CTgqyeZVddpEGkyyJ3BdVR04Cfn9A3g/reD7AOCVwC+SPL+qDp2E9iVJkiRJknQ3ZUFAksbnwqo6qPclyc+AWcAHgBdMsM09uzYOXLTUALh2IL+vAecDnwIsCEiSJEmSJC3BnDJIkhZBVf0DmA08Yti5jKaqrgH+BGyY5L7DzkeSJEmSJEnDY0FAkhZBklWB1YDLB7Y/s1tf4Loks5P8Kslmffv36s31DzwYeFLf3P+zJjnNm7r35fvOv3OSU5LMSXJ5kkOSrDfK9T29u46bklyW5LAkGy/oZEnuneSP3XVvMsnXIkmSJEmSpAlyyiBJGp/lk6zefV4b+HT3+Uu9gCRPAX4BnAa8F1gO2I221sCGVXUd8DPggu6QvWmjDD7RfZ8zWckmWRrYBLgWuLLbtgtteqKjgbcCqwJvp6018Oiqur2LeznwHeBs4H3ddbwFODHJ46rqr6OcbyXgSOBhwFOr6vTJuhZJkiRJkiQtGgsCkjQ+L+5ePQXsXlWH923bGDgceENVXQGQ5G/AEcBWwJFVdQZwRrfv48AV/XP/L4Klu4LFUsA6tPUJ1gc+XlV3djHrAz8GXlFVt3Q53AjsD2wAnNPd2P8ycCGwRVXN7eJOoRUSXgW8p//ESVYE/o82fdICiwFJdgV2BZgxYwZnrLHoFy5NlrnLYJ/UtGKf1HRjn9R0M9375DIjI8NOQYvZnDlzGPHvrmnEPql+FgQkaXx+DXwOCO2G+6uBfZIsVVX7AnTv+yZZPcl2wOOAl3bHT/X/q/JI4Kq+77cD+wIf6W2oqg8CJFknyRbAFsDL+/I7h1a4WBX4fK8Y0B17TJJlgV5xoWd52qLFTwHOBM5aUJJVdQBwAMAGD12/Hn3luK5RmlJnrAH2SU0n9klNN/ZJTTfTvU+u8cKZw05Bi9nIyAgzZ84cdhrSPPZJ9bMgIEnjc1lVHd37kuQg2pP+X0zy46q6IsnDga8D2wBzaTfI/0R7+n6q/R14Q/d5LnBOVV3bH5Dk8cBXgE2BG2hTG/0J2LEvrFe4+OfgCXpTCg3YkjYt0W7AfsDu9E2jJEmSJEmSpOGzICBJi6Cqbk9yDm2anIcAVwCHAfcBngj8saruSPI44GWLIaU5/QWLQUlWpk1ndAWtIHBGVVWSF/CfBYHeKIN1RmljX+DaqvpQ3+brgKdU1WlJtgY+kuSHVXX54PGSJEmSJEkajqWGnYAk3Z11c+1v3n29JMlqwIbAMVV1QlcMCPC6BTQzm1ZAWBw2BFYDDq2q07tiwLK0NQH6/Z52k/+VSVbobUyyPm0UwOBoh9Or6rTu87uBZYEvTH76kiRJkiRJmihHCEjS+KyX5GXA0sDawEuABwGHVNU/AZJcCjwnyTuBW4GdaOsIAKw4SpuHA3sl+Sxt/v71aE/g7z0F+f8duBF4TZIrgBWAXbjrBv+KAFV1Y5I9gAOBk5N8g7ZOwBuBOcDH53eCqrqku5YPJzmgqo6bguuQJEmSJEnSODlCQJLGZxvge7Qb5e+gzdP/FmDnvpjtaXPyfwj4MHAJbbHdO7p9gz4JfJpWXPj/7d1tiFx3GYbx6yYvGmusLU1rQ6VBLChVG0OUWJJ2SRtM0agJiIVK0XwQJIgvIDZFBT8osYhf1FDiW61aIVCE+kZItUuqpbVRiVRpRbTRFIsopNEoWMrjh3OSLtvpZtKd3XPIuX4w7DnPzgzPsjf/GeaZc87XaI4mOLkQzVfVcWAbcBT4fPs3PAjsmNH7qfveCdwAPNX29xHgMLCuqua8aDBwG3AM2NsegSBJkiRJkqSOeYSAJI2hqh4HMuZ9jwDXjfjVyDW3qp4Gdre3F6yqxu3vPpqLAM/2nMdX1QHgwBmeb82I2n+AV47TjyRJkiRJkhaHAwFJUqeydAkX73pX121Ipy2dnubid0913YZ0mplU35hJ9Y2ZlCRpfJ4ySJIkSZIkSZKkAUhVdd2DJGnAkvwLeKzrPqQZLgL+0XUT0gxmUn1jJtU3ZlJ9YybVN2ZymC6vqlWzi54ySJLUtceqan3XTUinJDlsJtUnZlJ9YybVN2ZSfWMm1TdmUjN5yiBJkiRJkiRJkgbAgYAkSZIkSZIkSQPgQECS1LV9XTcgzWIm1TdmUn1jJtU3ZlJ9YybVN2ZSp3lRYUmSJEmSJEmSBsAjBCRJkiRJkiRJGgAHApIkSZIkAJJcmGRLkou67kWSJEmT50BAktSZJF9P8kCST3bdi4YjySVJ7m+3lyX5YZvDnWdTk+YryflJfpLkYJLvJ1k+al0ctybNV5JLgR8BbwbuS7LKTKoP2tfu37TbZlKdSbI0yV+STLe31yf5TJKHk3x5xv3GqkmTlGRvkm3ttmulnpcDAUlSJ5LsAJZU1dXA6iRXdN2Tzn1JLgC+BZzXlj4EHG5z+PYkK8+iJs3XTcAXq2oL8CRwI7PWxVFrpeunFtCVwEer6rPAAWAzZlL98AVgxbj5M5NaQG8AvldVU1U1BbwI2EgzSD2W5Pok68epddO+zlVJNgGvqKofuFbqTBwISJK6MgXsb7d/RvMGWVpozwDvAU60+1M8m8MHgPVnUZPmpar2VtXBdncV8F6euy5OjVmT5q2q7q2qB5NcQ/Oh1Vsxk+pYks3ASZrB6RRmUt3aAGxP8vMk36UZnN5dVQXcC2wCrhmzJk1EkmXAV4HHk7wT10qdgQMBSVJXzgOeaLdPAJd02IsGoqpOVNVTM0qjcjhuTZqIJG8BLgD+inlUx5KEZnD6NBDMpDqUZDnwaeCWtuTrtrr2MHBtVW0EjgMrMJPq3s3A74HbaAb6uzCXmoMDAUlSV/5N8wYa4KX4mqRujMrhuDVp3pJcCHwJ2Il5VA9UYxfN0VAbMJPq1i3AV6rqeLvvOqmu/baq/tZuP4qZVD+8EdhXVU8C3wEOYS41B//RkqSu/IpnD0m8Cni8u1Y0YKNyOG5Nmpf2m6/7gd1VdRTzqI4l+USSm9vdlwN7MJPq1vXAriTTwFpgG2ZS3fp2kquSLAG203zD2kyqa38EXtVurwfWYC41hzSnL5MkaXEleRlwP/BT4AZgw6xTuUgLJsl0VU0luRz4Mc25XK+m+TbsZePUquqZLnrXuSPJB4HPAUfa0jeBjzFjXQSKWWvlqJrrpyahvfD6fpqLZD4C7Kb5lqGZVOfaocA7GCN/o2pmUpOQ5HXAXTSnVLsH+BRN1g4DW9vb0XFqVfXnxe5f56YkK4Fv0JzyZxlwI00+XSs1kgMBSVJn2g8etgCH2sMbpUWXZDXNN2MOnHoDPG5NmrRR6+K4NWkhmEn1jZlU3yRZAbwN+HVV/elsatJCca3UXBwISJIkSZIkSZI0AF5DQJIkSZIkSZKkAXAgIEmSJEmSJEnSADgQkCRJkiRJkiRpABwISJIkSZI0AUnWJlnbdR+SJEnPx4GAJEmSJEmTsba9SZIk9VKqquseJEmSJEladEleDNwBXAYcB24CbgdWA8eA9wO3AtNVNZ3kfe1D1wDLgI3A+cBW4MPA9vb3T1TVdYvxN0iSJJ0NjxCQJEmSJA3VB4AjVbURuJvmQ/1Hqupa4A/Azjke++r2fncBm6tqN7AH2OMwQJIk9ZUDAUmSJEnSUL0G+GW7fQdwKfBQu/8Q8NpZ918xY/vO9uffgeUL1J8kSdJEORCQJEmSJA3Vo8Cb2u1b2/0N7f4G4HfA/4CVbW3rjMeeHPF8/wVeApAkk25WkiRpvhwISJIkSZKGah+wLsk0sI7mKIErkxwCrmj37wE+nuR24J9neL6DwI4kvwA2LVDPkiRJL5gXFZYkSZIkSZIkaQA8QkCSJEmSJEmSpAFwICBJkiRJkiRJ0gA4EJAkSZIkSZIkaQAcCEiSJEmSJEmSNAAOBCRJkiRJkiRJGgAHApIkSZIkSZIkDcD/AfqsdLDKvt1kAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(20,15))\n",
    "sns.countplot(y='产品型号名称', data=order, ax=ax)\n",
    "plt.grid()\n",
    "plt.yticks(fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 卖的最多的型号是Easton-Z5,一种棒球头盔\n",
    "2. 卖的第二多的是Bat Pack，一种背包\n",
    "3. 卖的第三多的事HotDot-AHD12CY一种软式棒球\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 产品名称\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YV9k4GngP8CxK/fDWmXlzvcaE2reMiDUyc15/12jr1zbAizPz+A771wEOz8yPDNDGwcDBALNmzeSqWdsOdllV8ydNc7y65Jh1bzjG7G99fcPTmR4wZ84c+sbR7x0Ojln3HLPurO7xGrcBud549tb6dVJ9bQPsxLIl006rr5ZrG5+XKzmIiFmUGuTmjWwTKCF7AstKIZ5AKaEYzMtrn6YBl0fEYZQa4re0HXdT2w16LT8HZte+PYlSfjELmBgRTwSmZealwJ7AcRFxaWZeUs/duNHOycCrI+LOzGyOxUMy83TgdICtt3xMbneH1RhDddWsbXG8uuOYdW84xmyT/V41TL0Z/fr6+pg9e/ZId6OnOGbdc8y6s7rHazw/KOQTlBpfgOMz89XAVyk307VC7oGU2eMX1O+b1e8f4eFjdwFwD/BA43UfcBvQRwnUDwI7D7F/rwfmUEotbgA+A/w/yiz262sfd6bUCE+mlIJcDKxZj3lZo60zgMuAw4CZwBXAT1q/Hfgbdck7yhJ2e0fE+hFxOKWU5BvA54fYb0mSpJ42LgNyRDwSeBFwbD+7m2sPL8rM+Swrp1hQvy9uHNNyJGU2dg/g15Sl1V5KuQnwXXWJtIuos7qD9G8HYFfgzLrp/ZSVJjYHdqCE2eOAmygrS+wKvI0S4M8AIjPvazT59NrfYymBfWpti8xcAhwC7BMRe9c2ZlBu1Psk8FngAJd4kyRJ48W4DMiZeRPwHyxbqaHdlPp+RkQkJdgC3FG//zclZDbb/Elm/iAzz6OuO5yZ52bm2ZnZepDH+cCj6moSAzkauIT6EJLMvALYilICcj5lNvgjlKC+E6UM4x5KGH8q8MeI2DNq7UVmzmkG3MxcmJn3N75fDNwNfKpuuogSqj+dmQe3njQoSZI0HozbGuTMvKGud9yf24DHA5tSVpzYjTKbugPLyi9WZEb1/Pr+WsqNdg8TEU+j1BrvAWxZtwWlFvgVwBeBR1FKL1q+3/i8G2U2+UTKsnS3Dtap2v6XKStm3FbPuwd49lBv9pMkSRorxuUMcj/WjoiZlNpd6mzr8yh1vQ8AawPXZuZVmXl1fV3T7UUy8x+Uh3O8od4k2J9rKKtWnNc4L4GPAvtl5oGUpek2ZdnDTF5BubHuMcAlmfky4MmZ2TEcR8SkulIGlNno3Ws7reD9TkpA/2J0uAtQkiRpLBq3M8htzmx8bj2O+QzKsmx9lLWNf9Rto3XZtVY98dzMPJVS1nEWZT3jo+txJwDfz8y+zLyLMsO8nMy8MiIW1vKMxbWfa9bdawPr1c9b1eXgrmtvo5oaER+t1zixloy8H3hzZl7eysKZeWNEvAc4hVJa8rYa1CVJksa08T6DPLG+H0BZAu0YSsXB5Fqj+zxKfe/jgCU1eHYUEdMj4pnAI4FNKHW9f6CUU7RmjL9FuYnvyIh4dt22G3BRRAz2NL5PUcoffg38guVXovhFff22vp5W+xQR8fyIOJGyisValKXmjqAsS/d/wBfqUm3LqYH+a8DhwNcHmPWWJEkaM8b7DPK0+n5/Zt4ZEb+lLGc2ISLWpATPzSizx28EXhURXwO+B1xaV7QgIvajlEBsSVlv+F5KED4fuDgzb2hdMDMzIt4A/A44JyLeC2wNXJeZt7X1bzKNP2Iyc/fmzhpY7wf2b5ZkNNXrHVv7djJwambeXs9vtX3qAGN0IOWPhyspNcqSJElj2ngPyIso5QV/BcjMCyOij1L6cCildOHQzDy1PlHujcCbKbOviyPilMx8G/ArSk3wOcCXgJ9k5kI6yMy/RcQ+wHeAz9XN3+zn0Ck8fDm5plbAH3BmmxJyb87MOW39WEq5+ZCI2IUyk74JJXS3jlkQES+qy8FJkiSNeeM6IGfmXOBjbdsWR8SNlGXW3peZ19bt91FmlI+PiKdQVpn4ct33r4jYMDMf7OLaF9Qn3B1O+Xc4rp/DFlMeLtJJKxhPG+AYMvPqIXTpGuAgysz2GW3nG44lSdK4Ma4DcieZ+UXKcmqd9v+OEiSb24Ycjhvn/AN4+wD7j6eE8k77b6aUdKy0zLwjImauyO+QJEkaS8b7TXpqMBxLkiQZkCVJkqTlGJAlSZKkBgOyJEmS1GBAliRJkhoMyJIkSVKDAVmSJElqcB1kDauYNJlNDvvISHejZ/ytr49N9nvVSHejpzhm3XPMJKk7ziBLkiRJDQZkSZIkqcGALEmSJDUYkCVJkqQGA7IkSZLUYECWJEmSGgzIkiRJUoMBWZIkSWrwQSEaVksXL+DyU1860t3oGfM2eBmXn3r8SHejZzzpkB+MdBckSeOAM8iSJElSgwFZkiRJajAgS5IkSQ0GZEmSJKnBgCxJkiQ1GJAlSZKkBgOyJEmS1GBAliRJkhoMyJIkSVKDAVmSJElqMCBLkiRJDQbkKiL+q77W7GffNhHxpojYbCWv8dyI2DkihnXcI2L7iJg+jO2dEBFviIiNIuLQ4WpXkiSpFxiQl3k58IrMnNvPvj2B04FnruQ1vgZcChARG0bElyJik4FOiIh3RMQtA+yfAPwB+MBK9q3plcD2wG7AZyLilcPYtiRJ0qhmQAYiYgbwROAbHQ7ZDbg8MzvtH6r7gcWZuRR4DvBa4EeDzP4uBSY3+joxIqZFRNRNjwQmAT9sO2bGSvRzMTA3M88EPg8ctBJtSZIk9ZRJI92BkRQRPwVmAhPr64CIeAkwBZgKvBO4DHgu8OZhuOR9rQ+Z+a2IeDTwP8ChwMc7nLOUElhbtgcur/1vHveztu8LgGn1uHWB24F5QNb9AaxBCenn1uMXZ2bWa7YaeyuwtAbueZnZ7IskSdKYM64DMmXGeCrwKuAO4P8owXBqfV0PvJ4Snj8XEZ/rp43TMvOQiNiFEiwX1ff+JDApInas3y+o17skIp4D/D4zHxykz1cDjwXmU4Lzu4F9gac3jplGCb8tcymzwHOB/YAX1O9TgN/WNh8By4XuoyLiqLZr7wL8ZpD+SZIk9bRxHZAz87MAEXEE8MPMXC4AR8Qk4G3AOcDRbafvC3yIMvsK8B1goyFe+vIO27cHroqILYCfUQLtusAGEXEVJfS+Drimfl5MCct/anabUjpzXeN3LgTOqL9pe2B2Zp7d+J17Aktqe0uBCyklGyfUQyYDawF/HuLvkyRJ6lnjOiADRMS2wKOBd0fEScDSzHx73X0YpcZ3/8y8uu28rYGFwEV10ytZVg6xpMPlDgTeBOwD/Ltum8DyM9YA9wKfpMwSH0MJyp+ox9wIvA9o9bGl/Ua+JwF/7PS7mzLzD83vEbEIuLv9N0uSJI0HUUpOx6+IeD/w38AGwPmUm9N2rzfBXQRMz8ydI2In4D3AZzPzpxFxM/DPzHx2F9d6NWUm93mZ+bMhHL82cFd9bZ6ZCyNiKuUPm4XAsygz2Ftn5s31nAmU0okEJmTmvLY2PwAckpkbN7ZtA7w4M4+v368HvpGZ76vf1wEOz8yPdOjnwcDBALNmzdzpy58+bmgDIhZNWpfJi+8d6W70jDVmbcWcOXOYPn3YVjUcFxyz7jhe3XPMuueYdWdVjdeuu+56WWbu3L59XM8gR8REyqzuTzLzwYhYTJmtJTMzIp4LbFUPXw94BfCTGp7fTAmpzfZmUWqQmzeyTaDUBE9gWSnEEyglFIN5OeXfaBpweUQcRqkhfkvbcTe13aDX8nNgdu3bkyjlF7OAiRHxRGBaZl5KWcbuuIi4NDMvqedu3GjnZODVEXFnZp7WfpHMPJ2yDB6P3XKL3Piu7w3hpwng1g1ehuM1dE/a7wf09fUxe/bske5KT3HMuuN4dc8x655j1p3VPV7jOiBTZoS3Ar5UZ1GnAmvUz1Bmk/9eP7ceILKwrvTwQx7uAmCHDtf6G7Ad8CDwsL9UOng9MIdSanED8BnKWsxHUoLy5yg3511Rj/8ysBnwQkoYntxo6wxg28b3KyiraqwLHF+vdRKwI+WmxL1rbfargFdTbmj8/BD7LUmS1LPGe0B+dH3/MHAUpTRhKfCLuv03wEvq563r+0Dz+0dSQvZi4P8BW1JWi5gKzM/MxRFxEXVWdyARsQOwK3AaZSb5/ZQZ4c0pYf0k4DjgptrP91NuKLyUEoZfl5n3NZp8Ostqmg+p7UwDyMwlEXEI8K+I2JsSsqH8EfBU4LOUsoxOq3NIkiSNGeP9QSFHAo8CpmTmWpQA+oPMnFlfL2kcu2N9f0KnxjLzJ5n5g8w8j7rucGaem5lnZ2Zrxvl84FF1NYmBHA1cAlxV276CMtu9TW3jMuAjlDC+EyXs3kNZXeOpwB8jYs/WA0Uyc05zDePMXJiZ9ze+XwzcDXyqbrqIEqo/nZkHG44lSdJ4Ma4Dcmbel5k3Zuai/vbXG96IiCnAi+vmF63kZc+v76/tdEBEPI1SQnFcY1tQaoG/AXybMtM9n3IDH8D3Katn/AF4A/AX4ESGuPRcbf/LlBrs2ygz0ecAz46INQY6V5IkaSwZ1wEZICI2iIgn1ifobQLsHBE/ioi/sazOeG9Kre6ZwFb12BWSmf+gPJzjDQM8Dvoa4Iw6E906L4GPAvtl5oHAAcCmwBb1kFdQbqx7DHBJZr4MeHJm3tqpLxExqa6UAWU2evfazvy67Z2UMpEvNh5tLUmSNKaN6xrkiPgF8Iz69T7KeNwK/IoShq+MiGmUmdzbKWsYPwo4ISIubpYoDHCNCZQb93al3PR3KuUBHGdR1jM+uh53AvD9zOzLzLvoZ4Y5M6+MiIW1PGMxZSm31s2Da1NW2oAS4qfSeFhIm6kR8dF6jRMjIik1zG/OzMtbWTgzb4yI9wCnAHdExNtyvK8LKEmSxrxxHZApj2meCVxZw+DPKA/I+HDrgIj4DGWW9g2ZOS8i3kaZAT4nIvZpuxGOiJhOqVd+JGVG+m5gHUrwbj2Z7lvAO4AjI+L8Wv+7G/DOiNg4M28boM+foqx/vIDll5M7nrLEHCx78MhLgfPr7O9ulDKRV1GeivcE4IjaxxOBL9Tl2paTmadGxLOAw4FZEfGmzHxggP5JkiT1tHEdkDPzN22bJtAoO4mIo4BDge9S6nOpM6yHUVZ2+G1EvD0zz4+I/SglEFtSlli7lxKEzwcuzswbGtfNiHgD8DtK0H4vZZWM6/oJx5ObfcrM3Zs7a5nG/ZSn/Z1HP+r1jq19Oxk4NTNvr+e32j610zhR1oqeBVxJXSdakiRprBrXAbkfk+qLiDiV8jCQy4DXNEsLMvNz9XHMnwVOjYj/oJRlbEq5se1LlIePLKSDzPxbROwDfIeynjHAN/s5dArLr2fcblp9nzrIbzsQuDkz57T1YynlsdZExC6U2uZNKKG7dcyCiHhRZnZ6hLYkSdKYYUBe3jSWBc6PUm7Me3N7qATIzC9HxN+B+zPzFoCI2DAzHxzqxTLzgvqEu8Mp/xb9PaN5MeXhIp20gvG0AY4hM68eQpeuoazb/DvKWsrN8w3HkiRpXDAgNzSfxZ2ZNwL7D3L8r9u+DzkcN875B/D2AfYfT6kv7rT/ZkpJx0rLzDsiYuaK/A5JkqSxYtwv86blGY4lSdJ4Z0CWJEmSGgzIkiRJUoMBWZIkSWowIEuSJEkNBmRJkiSpwWXeNKwmTJrKkw75wUh3o2f09fXxpP0cL0mSRhNnkCVJkqQGA7IkSZLUYECWJEmSGgzIkiRJUoMBWZIkSWowIEuSJEkNBmRJkiSpwYAsSZIkNRiQJUmSpAafpKdhtWTxAn70+T1W2/X2OOhHq+1akiRpfHAGWZIkSWowIEuSJEkNBmRJkiSpwYAsSZIkNRiQJUmSpAYDsiRJktRgQJYkSZIaDMiSJElSgwFZkiRJajAgS5IkSQ0G5B4UEdMiYoeVOP8FEXFyREztZ99+EfGTiHjyyvVSkiSpNxmQe9PRwGUR8brWhoiY1cX52wJv6bBvGvB8YP6Kd0+SJKl3GZB7TEQcRAnIxwNfrds2B66KiE9ERHQ4b9OI2CQiZgJTgQTWj4jHRLF2REwHptdTMiK2jIjtI2LbVf7DJEmSRolJI90BDU1ETAT+GzgKeEtmntLYfRNwNvAOygzwof008TXgOW3b/l3fZ9TPazX2/aXx+VzgpSvad0mSpF5iQO4BEbEN8E1ge+BXwCMj4lOU2d51gfWBDerhh0TELZn5wbZmDqP8ey8CXgW8H3gksCbwILA3cDdwELAPsAswD/g1cN2q+m2SJEmjjQG5N1xHCcEJPAp4LnAbcCfwD+BeYCHwALA/cG17A5n50IxwRNxWPy4GnkGZgb6LUne8JnBtZl5bj12rXkuSJGlcMCD3hiOADwJnZuaD7TsjYlPgj8DHM/O5nRqJiEcCTwP2ACYCt1Lq0M8G/g94KnAf8J3GaWsA9wzLr5AkSeoB3qQ3ykXElpRw/DJgSV3i7VFthx1DCbwXRMSEiNg7InZra+fnwI3At4AXUWajDwceVUP3ocA1wCbAxhExuZ46AVi6an6dJEnS6BOZOdJ9UAf1xrwLgSnA8zJzfkRcBEzPzKfUY14IfBd4fmZeEhGTKLPJ04DHZ+aiety7gEcAP6Qs4/buzJzUdr0zgVdQwvbPKbXIVwOfAI4D1s/Mu/rp58HAwQCzZs3c6XOfOW44h2FAa8/carVda1WYM2cO06dPH/xAPcQx655j1h3Hq3uOWfccs+6sqvHaddddL8vMndu3W2IxSkXEFOAUYCtgJ2BpXW7tSuBtEbFXPfQrlOXeNoiIdwBbUGqLt6SE1k8DZObxjbaf1XaticD/UG7eO5xy899/UeqRbwSeDjwR+GJE7JWZNzTPz8zTgdMBttpyi5x03znDMgZDMXvfH622a60KfX19zJ49e6S70VMcs+45Zt1xvLrnmHXPMevO6h4vA/Lo9RrgQOCflJUrtqDM7N5CuSnvg8AXKGH2IGBP4K+Um/a+TimVOJwakOGhWuUplAeFLKnbngmcDOwAnJqZn67bL8rMmyLiAuA9lKC+JeVmQEmSpDHLgDx6fY8STH8L/BL4PfD3zJxTyyruBS4H7gB+lpn/bp4cEb8GrmprczZwZv381/p+NWWJuPdl5v+0DszMS+vH/wNeDjweOC4zbxmG3yZJkjRqGZBHqcy8E9imw74fR8QpQB+wQX8rW2Tmxf2c+hPgT8BvgJNa14mIxwHHRsSlmfnU9n5ExM7Adpn5m5X5TZIkSb3AgDzKRcSBwHrAAkptccvjKI+Mfm1EtO60DEoJxVTgi5l5R1tzD1LKMeYDi+sDSFq2AbZr2walrGMapfZZkiRpzDMgj35vodQML6A8Ba+l9VjoDzW2BSUcT6WsVtEekDenlGvMo9QgN5dvm04J179oO2cSZS3kpwJXrOiPkCRJ6hUG5FEuM3fqb3tEnAi8PTNndtHW3yghuL/2vgS8vJv2JEmSxiIfFCJJkiQ1OIM8SkXEU4G5LF933LRePa7fG/kof/xMA/6ZmffWlS+eBNxPXeKtzeOAKRFxSD/7Jtbr3ZiZXxn6r5AkSeo9BuTR6xeUgLyE8ljo/tzFw2uGWyZSapH3As4H/gM4glLL3Km9ucCHO+ybApxHeTCJJEnSmGVAHqUys99a4ZVo778oT8eTJEnSAKxBliRJkhoMyJIkSVKDAVmSJElqMCBLkiRJDQZkSZIkqcGALEmSJDUYkCVJkqQG10HWsJo4aSp7HPSjke6GJEnSCnMGWZIkSWowIEuSJEkNBmRJkiSpwYAsSZIkNRiQJUmSpAYDsiRJktRgQJYkSZIaDMiSJElSgwFZkiRJavBJehpWixcv4Atf3n3Y2z3wdT8Z9jYlSZL64wyyJEmS1GBAliRJkhoMyJIkSVKDAVmSJElqMCBLkiRJDQZkSZIkqcGALEmSJDUYkCVJkqQGA7IkSZLUYECWJEmSGgzIkiRJUoMBeQRExLSI2DgiJndxzuSIeMYwXX/TiPhkRDxqONqTJEkaSwzIq1BE7BARGRGz23a9ALgF2LqL5g4FfhERbxiGrr0VeFt9SZIkqWHSSHdgLImITYD3AUdl5oPAvLprbtuh8+v7giG2uynw38B5wHkRsXGHQzMzbxukrQ2AQ4BrgaOGcn1JkqTxxIA8vLYBXgfMjog9gIV1+9KI+BbwbGARMLVuvyQillBm8tcEHlGD9UMiYgLwZWB94EWUmedOvgX85yB9/Ciwbn3Nj4jBftNfMnPbwQ6SJEkaKwzIwygzL4qI/wAuBHYA/tLY/SngDEpo3hE4jjKDewslIK9F/zPKnwF2A/4MPDsz727urHXMPwaewCAlE7XU443AV4GPDOEnnU0J9JIkSeOGAXmYZeZfIuKxlLD75sb2S1qfI2Jx/XhJZl7TXzt15vjE2sanKXXLv4yIPTLzunrMZOAbwC7Abpl5a6d+RcTmwDeB+4AjMvOOwX5LRCxiWZmIJEnSuGBAHma1PvgNwGHAOm379gEWA9vVTbtGxDaUkovfZ+YNjcP3oNQKH52ZH631zT8GLouI1wG/oYTjpwMvzcxfDdCnTYAfAbOAw4YSjqsJGJAlSdI4E5k50n0YE2owPgV4CXADcDxwCfAn4D8y89cRcQ2l1nhh2+nTgLdk5tltbW6amf9ufF8bOAd4LmUm+AHg5Zn5hwH69UjgImBL4CzgFYP8lD0z8wf13D8DN2TmHoP89oOBgwFmzZq50ymnfHSQS3Rvgw0eO+xtjgZz5sxh+vTpI92NnuKYdc8x647j1T3HrHuOWXdW1Xjtuuuul2Xmzu3bnUEePrcB91Nmjr+YmYsjYvu6b4OI+ADwtMy8a6gNtsJxlDvpdqSE2ycBSyk1y0uB19dw/tsObd9GmXm+Gji3tnEc8JW2454HnMzyK25MZdmKGwP183TgdIAtt9wi75vzraH9wC7ss89Phr3N0aCvr4/Zs2ePdDd6imPWPcesO45X9xyz7jlm3Vnd42VAHiZZpuJf17b5b8CjKTPGpwJbAa8eqJ2ImAr8B7A58DjgicDTgJnAP4GTgM8CCRwOHEBZ15iIuBO4Gbi9NvfGzLypHkdEbFG3356ZV7ddd5v6cUlj8zSGEJAlSZLGEgPyMIqIxwD3APdl5tLMXAhcX/cdC+wbEVMzc0HbeROAKSyr+f1/wK7ArZQZ4HMoayC3Qu2M+v6V+toGeArlwSNLgS2Ae2o47lbz4TEGZEmSNO4YkIdJREyiPHyj9b3ToQOtPfzlzHx9RLwemJCZ19c64IdqfAdxV2bOHMJxn4iIT3TYN63xeS0MyJIkaZwxIA+TWnPcWt5tPqUEAmB74DvAjcBjgWdRZ5WrR1BmnR9aMSIzb2zsX0ANzq0NEdEH3JqZ+ze2fQx4zRC7218NcsvNtb31cQZZkiSNQwbkYdRc07jeWHcQZS3jb1CWbPsm5cEfz87M+fUR0mcDlwOvyMyl/TS7GFinUSMMZSWMGW3b1q/HDsXDapCb6mz4fq1jh9imJEnSmGBAXgUi4vnABylrFP8lM99Yt78J+CNwdi1x+DJwJ/CpDuEYSk3xy+urXfvyazf0c0y3ff8SZSZ6AvAg8P2VbVOSJKmXTBj8EA1FRDwqIt5ba4bPo6x//Fnqo5ojYivKg0NeTimzuIAyo7xz8yl7/TVNKbGI1gv4OXBW27b/GaafcgZlWbhjgSfXJwOeGhHHRMR6w3QNSZKkUcsZ5OFzH6Us4QpgvxosPwWsFxFnUtYf/q/6VLznUOqS9wfmRsRPgCsy895+2p3A0EssBvuDp7V6xoadDsjMnwI/bX2PiGm175OBTjf2SZIkjRkG5GGSmfdGxFNbpRK1BvmllPWMn0AJmefUYy+PiO2Ad1FWpziqnnNAZn6trelJDL3E4pZBunkr8C/gzRFxA+XGwQcpDweZS1mveUrjtQHwFmBd4OTMfHCQ9iVJknqeAXkYNeuIMzMj4p2UlSDOyrZnemfmHODYiPgQ5UEgT+0nHFPPX24Vi/7UVSwGXAqu9unlwAeAdwBrUILw1PoOJSS3XguAeymP0D5moLYlSZLGCgPyKpSZ3x3CMUuBX9dXf/sfP8RrvQ943xCO+z3wkqG0KUmSNB55k54kSZLUYECWJEmSGgzIkiRJUoMBWZIkSWowIEuSJEkNBmRJkiSpwWXeNKwmTZrKga/7yUh3Q5IkaYU5gyxJkiQ1GJAlSZKkBgOyJEmS1GBAliRJkhoMyJIkSVKDAVmSJElqMCBLkiRJDQZkSZIkqcGALEmSJDX4JD0Nq0VLFvDhs17Q775jXnH+au6NJElS95xBliRJkhoMyJIkSVKDAVmSJElqMCBLkiRJDQZkSZIkqcGALEmSJDUYkCVJkqQGA7IkSZLUYECWJEmSGgzIkiRJUoMBWZIkSWowIHcpImZFxCNWYftPiIjHr6r2B7juMyJiv9V9XUmSpNFm0kh3oAd9HXgaMGMVtf8VYBGwyypqv5PjgadFxKaZectqvrYkSdKoYUDu3jxgfutLRATwGOB5wHbAmsAa9X1NYAlwC/AT4JuZmYO0/yCwePi7Pag+SvDfCTgXHvptk1uvzLx3BPolSZK0WhmQu7eE5QPsVOCaIZx3IPBDYE5ErAGsB9ybmXPbjsv6Wk5ETKjXWpyZi1ak4xHxPODCQQ77QcnF/Z6/RmbO73enJEnSGGFAHoKIWItS9rCIEpCX1tnVSZRZ4ndRZpWX1lNeArwY+G/grnrcNGBB3f804KLadqdrdppp3gv4bj1mC+C6Abp+Q2Zu0fi+pL4fDpw1wHlNU+prMsv6L0mSNGYZkIfmh8Bz2ra1wvCSzFxuHCNiY0pA/lpm9je7fDnwFEqobi+n+ErddmDb9omU0o1mIL4DeE3j+xnA1cBH6vc5bW20AvL9mXln7evewLmZuTAiXgl8Edg3M1tlFk/MzCv7+Q2SJEljkgF5aI6s7/OA/wN2AJ7LstDalcy8D/h9f/siYi6ljOLqIbTzIPDVxrlnALdl5lc7ndJ8j4gdgLMpwfq1wEJKGceCun9D4I8R8d3M3HsIP02SJKnnGZCHIDMvbX2OiAcoAfYvI9ilFfXQrHd9P4BSNvLfbdtbNc77AQGcNlCjEXEwcDDAzFkz2XLKvv0e19fXtyJ9HtPmzJnjuHTJMeueY9Ydx6t7jln3HLPurO7xMiCvhIiYAmybmZcP8fhpwCaUmehOK1VMLofGzA77J1Lqme+sM8jdaK17vaj25XWU0Pw02mqZ602Bb6HMJi8cqNHMPB04HeAxW22R1y78dr/HvXL2+V12d+zr6+tj9uzZI92NnuKYdc8x647j1T3HrHuOWXdW93gZkFfMWhFxCrA3pZ74hUM8bzvgd0M89o5B9j+fwVekaNcKyAuAQ4ENgd8CX4uI6cCdjWNfDTyecpPhhRFxLPChISxTJ0mS1NMMyEMQEesDuwIvoqx3vC7wVOBE4JtdNPVn4LGUtY47LdX2Pcrs8j4d9rdWzri9i+u2TKvv6wPHUNY+fh7lxsDTgVPq/vWA44C/Ak8HPgccS1nP+bMrcF1JkqSeYUAeRERMBf4JrAP8HbgXeDAzd+q2rcycxyBrJkfEIkqN850DHbeC1q3v61Bmi9+dmUsj4o3Avyk3Dh5GeYrfdODgzLw/Ig4A/gB8YRX0SZIkaVSZMPgh41tmLgDeC8zOzMcBlw1HuxHx3og4p4vjfxgRJ67kZTes72dQVuHIiNgW2IISfmfV/VcBuwPX1v1PBL6RmUuQJEka45xBHoLMHHAVhxW0LbBbF8dvx8o/gvrRwH2ZeU9EHEgpo1hIqUlOygNBAE5iWQlIaym7zwBHrOT1JUmSRj0D8shZyrJl14ai30dQd+mplAeJkJnHA8c3d0bEy4HvAC/LzL6VvJYkSVJPssRi1ej/+dEjqC5JtzNwxUj3RZIkaTRzBnmIIuLxlPHahrKOcafj1qOs/ACdV6qgtjUhIrZp274msLif7ZO76/HD7EFZxeLnK9mOJEnSmGZAHrqjKU+eg/pQjKaImEyZnX183fSPzLxhgPbWAmZQllLrT3/bp/Sz7SGZOdDM9bsoNcwXRsR/UFapuA94gGWlHo+o75u3BfTJta+TM9OALUmSxjQD8tB9GbgW+AXws/admbkoIo6n3Hj3a+DMgRrLzL26uXhEXE+5Wa5rdam6e4DvZebtEfE54AWUG/QWsnxt813ACW1NTKCE878BXS9vJ0mS1EsMyEOUmRcAFwxyzOeBz6+i62+xEucuAPZsPb46M/ccrn5JkiSNNd6kN46sooePSJIkjSkGZEmSJKnBgCxJkiQ1GJAlSZKkBgOyJEmS1GBAliRJkhpc5k3DavLEqRzzivNHuhuSJEkrzBlkSZIkqcGALEmSJDUYkCVJkqQGA7IkSZLUYECWJEmSGgzIkiRJUoMBWZIkSWowIEuSJEkNBmRJkiSpwSfpaVgtWLKAF33vlYMed97Lvr4aeiNJktQ9Z5AlSZKkBgOyJEmS1GBAliRJkhoMyJIkSVKDAVmSJElqMCBLkiRJDQZkSZIkqcGALEmSJDUYkCVJkqQGA7IkSZLUYEAeIyLihIh4Q0RsFBGHjnR/JEmSetWkke6Ahs0r6/tC4DMRcW9mfr21MyLWAh65ktf4V2Y+sJJtSJIkjWoG5LFjMTA3M8+MiF2Bg4CvN/bvCPxiJa/xQuD8lWxDkiRpVLPEYpSKiHUjYmFE3BcR99bXfXXb/hExPSImR0TUU5YCrc9vBV4cETMiovVH0D31/Z2ZGd28gDfUc+9dLT9ekiRpBBmQR6+5lFngA4EfA1k/vw74LXA1pZxiaUQksDlwVP08F5gP3A/sXNub3+lCEbFXROwTEWu0bT8kItZrbFo4HD9MkiRpNLPEYpTKzIXAGQARsT0wOzPPbu2PiD2BJZTSiqXAhcAPgRPqIZOBtYA/1+9Ll50aRwPTMvP/1W0HA88CHgf8qx60DvBp4KXAt1rdGt5fKUmSNPoYkHtUZv6h+T0iFgF3Z+bVg50KrAe8PSK+ClwLPAP4GnBwRDwhM/cDnk35PwyfBdYd5u5LkiSNWpZY9KiI2CYi3jXA/nXqTHF/jgGuAz4B7ALMoMxWLwL2jYjnAM8H7qbMSkuSJI0bziCPYhHxJMqNd7OAiRHxREppxKXAnsBxEXFpZl5ST9m4cfrJwKsj4s7MPK3ZbmbOj4i3AecBGwI3UFa4+A2wP/C/wPrAOZm5aNl9gB37eTClTIOZs2ayN7sP+tv6+voGPWY8mDNnjmPRJcese45Zdxyv7jlm3XPMurO6x8uAPLqdAWzb+H4FcB+l5OF44PXASZQl3CYCe0fEEcCrgFcD3wA+31/DmfnjiPgRsAfw0cxMYFGddf5uPeyNQ+lkZp4OnA6wxVZb5Dn8ZNBzzpv99UGPGQ/6+vqYPXv2SHejpzhm3XPMuuN4dc8x655j1p3VPV6WWIxuT6fcbHcscBswlbJaBZm5BDgE2Cci9gY2o5RK/BD4JKV2+IDMXDxA+xfW94dWp8jM7wE3AQsoq2VIkiSNKwbkUSwz5zQDbmYuzMz7G98vptQJf6puuogSqj+dmQdn5lI6iIipwBGU5d/eGxGPrtt3ozxxbyq1bEKSJGk8MSD3sPqQkC9T1j2+DbgUOAd4dvuaxv14G6Vm+UWU/w5OjIgplBv3/gz8DjgmItZeRd2XJEkalQzIPSYiJjVC60eA3YFXsOxBIO8EtgS+GB3urouITYCjgS9mZh9lveNzgY8C2wHvAT5IuTnw/avml0iSJI1OBuTeMTUiPgpcT1mv+F2U8Pq2zLy8dVBm3kgJuK8APtVPSA7KDXUB/Fc9512UmuN3AWdm5nmZeS5wAeVBJJIkSeOGq1iMUjXY7ga8mLIqxVrAEyh1w5sAJwJfqCtILCczT42IZwGHA7Mi4k2N3bMo9cVHZuatjWttCfy9ntNqZ/e6//XD/PMkSZJGLQPyKJWZGRHHUoLrycCpmXk7QES0Zv5PHaCJAylh+EpKjfLMuv124AV1WbeHrgX8d0R8pD7iut2MlfoxkiRJPcSAPLodCNycmXOaG+vqFJ8EiIhdgAMos8rNFS4WRMSL6nJwRMSajX1JP9rDcUQcBcymrIxBs31JkqSxyhrkUSwzr24Px/24BjiIsurEGW3nL2l83WAFuvBbYHvKE/aOyMx/rkAbkiRJPcUZ5B6XmXdExMzMfHCQ4y6m3JjXTds/pcxMS5IkjRvOII8Bg4VjSZIkDZ0BWZIkSWowIEuSJEkNBmRJkiSpwYAsSZIkNRiQJUmSpAYDsiRJktTgOsgaVlMnTuW8l319pLshSZK0wpxBliRJkhoMyJIkSVKDAVmSJElqMCBLkiRJDQZkSZIkqcGALEmSJDUYkCVJkqQGA7IkSZLUYECWJEmSGnySnobVgiWL2OM7/zPS3RhxP9rrvSPdBUmStIKcQZYkSZIaDMiSJElSgwFZkiRJajAgS5IkSQ0GZEmSJKnBgCxJkiQ1GJAlSZKkBgOyJEmS1GBAliRJkhoMyJIkSVKDAbkHRcS0iNhhFbZ/QkS8ISI2iohDV9V1JEmSRiMDcm86GrgsIl7X2hARs4ax/VcC2wO7AZ+JiFcOY9uSJEmjmgG5x0TEQZSAfDzw1bptc+CqiPhERMQwXGYxMDczzwQ+Dxw0DG1KkiT1hEkj3QENTURMBP4bOAp4S2ae0th9E3A28A5gGnBo27nrArcD84BsbQbWAF4LnAssABZnZgJL636AtwJLI2IGMC8zFw/3b5MkSRpNDMg9ICK2Ab5JKXv4FfDIiPgUMB1YF1gf2KAefkhE3JKZH2w0MZcyCzwX2A94Qf0+BfgtcDXwiHqt1jlHRcRRbV3ZBfjNcP42SZKk0caA3Buuo4TgBB4FPBe4DbgT+AdwL7AQeADYH7i2eXJmLgTOAIiI7YHZmXl2a39E7AksoZRWLAUuBH4InFAPmQysBfx5Vfw4SZKk0cSA3BuOAD4InJmZD7bvjIhNgT8CH8/M53bbeGb+oa29RcDdmXn1inVXkiSpd3mT3igXEVtSwvHLgCV1ibdHtR12DDARuCAiJkTE3hGxWxfX2CYi3jXA/nUi4ugV6b8kSVKviXJPlkajemPehZRa4edl5vyIuAiYnplPqce8EPgu8PzMvCQiJlFmk6cBj8/MRfW4J1FuvDsI+E/gecC0zLw0Io4EjqOUXlwSEdcDfZn5+nruGcCrgUMy87R++nkwcDDAzFkzdzru9JNWxXD0lK3W3XhIx82ZM4fp06ev4t6MLY5Z9xyz7jhe3XPMuueYdWdVjdeuu+56WWbu3L7dgDxKRcQU4BRgd2AnSp3xYylB9G3A3vXQrwBnUVai2KK+ZgM7AIdn5qdre1cB27Zd5r7MXLcG8T8BCzNzx4i4CVintvUq4CTgG8BrBlvFYoutHpNP+PibV/BXjx0/2uu9Qzqur6+P2bNnr9rOjDGOWfccs+44Xt1zzLrnmHVnVY1XRPQbkC2xGL1eAxxIufnuV5QVKK6irEJxL6XsYgvKShYHAacDe9XvX6cs63Z4o72nU262O5Zyg99UYHOAzFwCHALsExF7A5sBMyg36n0S+CxwgEu8SZKk8cCb9Eav7wHvoSzD9kvg98DfM3NOLau4F7gcuAP4WWb+u3lyRPyaEqgByMw5dXvr+0JK+G59vzgi1gM+VTddBOwKnJSZb1sFv0+SJGlUMiCPUpl5J7BNh30/johTgD5gg/5WtsjMi7u5Xn0C35cpM9W3AZcC9wDPjog1MnNed79AkiSpNxmQR7mIOBBYj/qku8aux1HKJF4bEc2n402p27+YmXcM0vYkYM3MvB/4CKXeeRfgO/WQd1LWPv5iRLwyLViXJEnjgAF59HsL5ea6BcCixva16vuHGtuCEo6nUuqHOwXkqRHxUcpjpk+sAfv9wJsz8/JGGcaNEfEeys2Cd0TE2wzJkiRprDMgj3KZuVN/2yPiRODtmTlzsDZq+cRuwIspq1KsBTyB8gCSTYATgS9k5un9XP/UiHgW5Ya/WRHxpsx8YMV+jSRJ0uhnQB4HMjMj4lhgS+Bk4NTMvB0gIlormZw6QBMHArOAKyk1ypIkSWOWAXmUioinUsJop6XV1qvH9XsjH2UJv2nAPzPzXkrIvbm1mkVLZi6lLOVGROwCHECZVb6/ccyCiHhRXQ5OkiRpTDMgj16/oATkJUCnut+76nH9mUipRd4LOD8zrx7CNa+hrKn8O+CM5g7DsSRJGi8MyKNUZk4ZgWveEREz+1s2TpIkabzwSXpajuFYkiSNdwZkSZIkqcGALEmSJDUYkCVJkqQGA7IkSZLUYECWJEmSGgzIkiRJUoPrIGtYTZ04mR/t9d6R7oYkSdIKcwZZkiRJajAgS5IkSQ0GZEmSJKnBgCxJkiQ1GJAlSZKkBgOyJEmS1GBAliRJkhoMyJIkSVKDAVmSJElq8El6GlYLlizmJWd/fqS7MWTn7nPQSHdBkiSNMs4gS5IkSQ2DBuSI2DQi1mnb9vKI2GPVdUuSJEkaGUOZQd4PuCQiToqINSPiQmA34E+rtmuSJEnS6jfUGuQ3AUuAbYBPATcDMyNibmbetao6J0mSJK1uAwbkiHgBMBFIyqzx1Pp5R8rs80uBnVZtFyVJkqTVp2NAjogZwEuAZwFPAU4H/ga8D/hMZl4dEf+MiAmZuXS19FaSJElaxTrWIGfmA8DHMnNH4OPAW4E7gd8D/xsRJ2fmVwzHkiRJGksGq0H+v4g4CrgceCSltOKFmblnRKy5qjsnSZIkrW6DBeTvAN8G/g08CTgf+F1EfB+IiFg7M5+zivsoSZIkrTaDLfN2BXAe8DJKacVbgbnA74B9DceSJEkaawYLyP8ETqGsXPHBzPxqZr6csgbyI1Zx30ZcRDwhIh6/AufNioieGZ+6vvXeETFtpPsiSZI00gYMyJm5KDP/lcWPGtu/m5n/XPXdG3FfAb6wAud9Hbh6mPsyqIjYKCL2iYhdujx1V+Bs4H9XQbckSZJ6yqAPComImZl5Z+P7/wEL69epmfmuVdW5UeBBYPEKnDcPmN/6EhEBPAZ4HrAdsCawRn1fk/IQlluAnwDfzMys520AbA0EMKUeOx1YF1gf2BDYGHgUsFX9DnBlROyUmQ/1PSKmUNaxXpCZrX+/lrcCdwDH9PdjImIyMK3DuZIkSWPKUJ6kd1VEvD4zf1y/zwbeBRwPHLGqOra6RMQawHrAvZk5t2131lf7ORMoYXNxZi7qp9klLB+spwLXDKE7BwI/BOY0tp0PzGh8nwvcX9tcD/gRcCXwU0rIvYOyHN+0tnZeBXyx9r/T9e8bYB/AK4FvDOF3SJIk9ayhBOSrgRdGxFaZeTJwd2b+PCLuycyLV3H/VoenARdB5+AYEQ8LydVewHfrMWsBi+prCbC0zhxPosz8vosyq9xaN/olwIuB/wbuqsdNAxa0Gs/MuyJiG8oM8hzggda60xHxYeBo4C2Zef0Qfuc5wMW1/YWU4L8lJVj/FDioHrc+cBnl5szD6raJlBnse4dwHUmSpJ42lIC8MDPfERGfiYh9VnmPVr/LKU8KnM/Dyym+Urcd2LZ9IqVE4rrGth8C7at6tMLwksxcbqwjYmNKQP5aZnacXc7Mfw/hNwwqM++nzDy3rr8O8HnK0xFflZkP1l13RsSbga8CV2bmh4fj+pIkSb1iwIAcETvV93Up6yA/njJDCv2UHvSizLyPsoTdw0TEXEoZxVBuuDuyvs8D/g/YAXguy8L0ComIHSk3Uy6gzEy3bFDft2xbfaJVa3xNZt7Toc2NgB8D29ZNczrMnn8oInYGjszMv6/ob5AkSeolHQNyRKwPfIhyE9gBwPso/4v92xHxX5Rg9l/Ah33cNGTmpa3PEfEAJVj/ZRia/gbwuAH2X9hh+0uBc9s3RsQewGmUf1coQb7TjXdPB04FXhoR51NKMb6UmXcNod+SJEk9qWNAzsy7gT0i4gDg9cBXMvOBiPg7pUb1p5RVLHoyHNdZ100oM76dVqqYXA6NmR32T6TUDd/ZKFFov84UYNvMvHwFu3oA5d9pQVs/3wocDLyA8qTDlqn1tdysd0Q8mzLL/WLKkm6XACd2uGbUNv4KPBk4CXhR3feJhx0ccXDtCzNnzeLlE9cZ6m8bcX19fSN6/Tlz5ox4H3qNY9Y9x6w7jlf3HLPuOWbdWd3jNWgNcmaeGRH3AYdS1sn9MrBOZv5wVXduFduO8kTAobhjkP3P5+EzuWtFxCnA3pQ65xd2170iMy/rb3tEtPr09yHepLcpsBPwmsz8akS8vm7/WX/NU4L/xMycBjwvIrYD5vX3B1Fmng6cDrDFVlvmd5fcN4TujA7nzt53RK/f19fH7NmzR7QPvcYx655j1h3Hq3uOWfccs+6s7vEayk16ZOa5LPvf9T+hLC/W6/4MPJay1nF/S7UBfI8ya9vp5sTWChW315KUXSkzrc+jrFX8VMos7Te77VxEzKDUHM9fkVn6OnM9jbLyRWbmNyLinH7WMd4sM+f3c/5UYFpERD3/qm77IEmS1IuGFJBb6oMroKyXOwt4sJ+1g3tCZs5jkLWJI2IRpZb4zkGOmwrcDKwD/J1Sq/1gZu60El28iDLjO9C6xQDXDbJ/E+BWgA4P+Zg3yPnr4fJukiRpHOkqIAPfoYTKoJQMvIbON4n1jIh4L/C0zNx7iMf/EPhHZr4DIDMX1DaurmtEf5tyg9vK+DCwNg9fvaJlf8rM9mH0XwIylTKDPFi9w2Y01l5umFZf9/ezT5IkacwabJm30ynhaRJwChCZeWDdd0lm9nw4rrYFduvi+O1ou7EvM08bzg5l5ncH2l+XfwM4byg1yBGxHvBoyooViykzy1CWixvo8dFbR8QkSuC+PzP/Mdi1JEmSetlgM8jbU2YqXwE8EiAiHklZl3dqRDwyM29atV1cLZay7KEeQ9HvI6hHuWcAP+hn+xVdtHEW5b8HSZKkMWvCIPvnZeYNlEchJ+WmtSMpj02+FHj/qu3emDZg4e8q8FNgQ2AtysNEJgJnAncD22RmtF6UNZKvoyzp1roRcSPK0nKSJEljWrc1yDdTSi6SErIGC9i9YhIwISK2adu+JrC4n+2T+2skIh5f29qGsr5yv2q5Q6tGudMKGp3OnUAJq7t0c369KXFebWMK8DHK8nS7Zebf6vaJwAxKOcUvgXdSyk8OyMzbu+mnJElSrxosIE+uy5etRZnxPAw4pn7+VP0+FqxFCYZ/7bC/v+1T+tl2NOXBHlDXBW6KiMmUkobH103/qDP0Q1KD7ZUse7LezcAtQz2/trEl8GtgFnAP8IP60JQ1Wf6R2EspN+jtDlwZEftl5i+7uZYkSVIvGiwg/xs4oR73S4DMvBggIu4bK4EpM/fq5viIuJ7lw2TLl4FrgV/QzwM4MnNRRBxPuSHw15QSh276ubCe/3LK0ww/1+0ayZl5bURcTSmhuL6+t163U8pp7m49GTAi9qP8IfTnbq4jSZLUqwYMyJn5iub3KL5AmUHeOiK+0FrVYjzJzC06bL8AuGCQcz8PfH4lrv1Z4LMren5t49ldHPutiDi7Vx8pLkmS1K1Ba5AjYmpd5zeA91IehJHAeyg3emmMMxxLkqTxZMCb7OpNWz8GyMwE/icz78zMu+r7baujk5IkSdLqMmBAzswlQEbEFyPiaaupT5IkSdKIGcoybUF5QMTh9N7DMSRJkqSudAzIEbFRRJwBLMrMH2fma1ZjvyRJkqQRMdAM8v3AGQARMTMiXrZ6uiRJkiSNnI4BOTPnZeZPKCtdfI/y+GFJkiRpTBvKo6YzM58BEBFHRMRewE3AHzNz8SrtnXrO1ImTOHefg0a6G5IkSStssGXeAvh4Y9MXgB2ANwJ/iIijVmHfJEmSpNVusCfpJXUd5Pr9f1qfI2Ia8M6IWDsz7191XZQkSZJWn6GUWPQrM+cDxw1jXyRJkqQRN5R1kCVJkqRxw4AsSZIkNRiQJUmSpAYDsiRJktRgQJYkSZIaDMiSJElSwwov8yb1Z8GSJbz022evVBs/2HefYeqNJElS95xBliRJkhoMyJIkSVKDAVmSJElqMCBLkiRJDQZkSZIkqcGALEmSJDUYkCVJkqQGA7IkSZLUYECWJEmSGgzIkiRJUoMBGYiIzSJidkSsO8AxG0TEdhER9fsaq62DkiRJWm0MyMVewEXANgMc8wrgj5mZEbEbcFNEbL8yF42IjSJin4jYZZDj3hMRBw+w/yURcVlEbDTE624cEdsM8bVJt79LkiSpl00a6Q6MEg/W9wUDHDO/sf9XwF3AScDs1gERsQGwNRDAFGBNYDqwLrA+sCGwMfAoYKv6HeDKiNgpMxd3uPZBwI3A6R32/xN4IvAx4A0D/IaWY4GOgbvNZ7s4VpIkqecZkIul9T0BImJ9SqhdWF9LKWE3I2JmPfbVwF0RsQVwe2bOrdvPB2Y02p4L3A9MBdYDfgRcCfwUuKO+7gSmAXM69G9B7Ue/MvMvEXEO8PSIWLPRl05a+ycPEMqJiKT8YSBJkjRujNuAHBGTKLO58yjhF2CdiNiMMgv7wQ6n3tHPthcBP87MuyJiG8oM8hzggcxcWq/3YeBo4C2ZeX2X3V0KLGnr/ybAMxqbzge+DexRy6QBrsjMf/TTXqutx0XEkn72t19bkiRp3Bi3ARnYAmgPj331/RHA1yiztgsoIXF/SgnDFo3jJ1Jmhu9sbcjMfw9H5yLiCZR/n0XAZGCtiNi2Xu92SknFt4B7ePjs8gRgFvA2Hv4bm64ajr5KkiSNJeM5IN8GvJ4SgJ8BHA68mxI+b6OE3+ZNjK364/YyiMXNMoWI2LGet4DlZ303qO9bRsS0xvYplNB7TWbe09j+GeDJwGJgbUpt8y8opRgfAy6px+2dmX3NDtVZ8JsYuKYahlZiIUmSNK6M24CcmQ8AXwZoBNafZuYf67ZLKQG13by27ycC72x8/wbwuAEufWGH7S8Fzm30b3brc0T8Hrg5M1/e2PbsAa7R0jH8Vosa5RiSJEliHAfkDtaIiP/MzG9SbmT7JeVmPIB9Kas/bNs4/nc8/Ca2AyjjuoDlA+pbKatBvABolmFMra+rh9LBGubXpt5QOIjB6oe3o622uc1fh9ing6krXcycNYuXTZw4lNM66uvrW6nze8mcOXPG1e8dDo5Z9xyz7jhe3XPMuueYdWd1j9e4D8gRsSalfAHg55T1jb9FCY6LWzfURcSdQDZvsKs3uC1XxpCZl3W4Tuvmvr8P9Sa92rftKCtqbBIRlwHbAx+ihHeAiwaYBR5sneu/DVJiMZRukpmnU5eg22KrrfJ7Swa7729gP5g9e6XO7yV9fX3MHke/dzg4Zt1zzLrjeHXPMeueY9ad1T1e4zYgR8Q6lCXXdqLUAUNZveJr9WEgAM9rr8PtVJcbETMooXp+a+WKLvszhVJf/ABlRvlySnCfQJktvp4SQi/IzH9GxJ711N2Af7U1tw3wncbv6sQSC0mSpDbjNiBn5n0RcRtwJKUU4STgz5nZDMC/Bg6sn/cCjqEE6pZLGp8vau0bJHReN8j+TTLz1oj4CnALZb3k71BqkE9rHlfff5GZy81iR8QtwH7AFQNdiGEqsZAkSRpLxm1ABsjMvQEi4vUdDpmbmVfXY24pp+RDtcJtawh/mFIb3L56Rcv+wD7AYfS/lvJUygzyfbVvxzWu01/ftgDuaA/H9dz7KGsiPyQink6Zjb6P8lS/oVqvru28DjAjMzvdZChJkjQmjOuAPARr1nAIZcY2Gt+hLAUHQGZ+d6CG6vJvAOetwINC+vMU4PddHP8x4Dlt24ayDvJr6wvK47VnDnCsJElSzzMgD2wXHl5mMOJlB/Upes8GPtDFaf9JWcP5oRrpiNgdmJWZZzbaDuB/gW9n5m8b22dQbhaUJEka0wZb5WC8+2lmRmYG5Qa+B1vf67bbRqhfB1Nmr88a6gmZeXtmzs3MpRExJSI+DvwY+HxE7NA4dC/Kus6/iYgfRMT29fwHMvOmYfwNkiRJo5IBuVijvi/qZ9tAJjPIesQRMaHO+O7SzzUGFBEvj4gPUJZ2e7Bu2wI4AvhRZl471LbquRMj4jXAlZSnBl4BPDMzH7qZLzPPAZ4AnAnsAVweEadFxIbdXEuSJKlXjesSi4h4DuXGuT0oT8i7obF7OmXJtf7O2xd4GuVmt1sHaH8KJYy2nqx3M2VliqF6JaU04p/AyRGxBmVpuimUgDtkEbE+ZVWOrSmlFu8CPtXfOsiZ+Xfg1RHxMeATlBnrzYEXdXNNSZKkXjSuAzLwF8qjoX8HHJqZcxr7ZtA5zG4C7A58EPhSp8Yzc2FEHA+8HLgM+FyXayS/B3hv28NJXg9smZl/66IdMvPuiDgT2AD4aGYOWh6SmVcBz4+IVwAXdHM9SZKkXjWuA3Jm3sGy9YTb923e9v1L1DCcmSdR1k0eyjU+C3x2Bft3Yz/bLgUuXcH2PriC5w251lmSJKnXWYMsSZIkNRiQJUmSpAYDsiRJktRgQJYkSZIaDMiSJElSgwFZkiRJajAgS5IkSQ3jeh1kDb+pEyfyg333GeluSJIkrTBnkCVJkqQGA7IkSZLUYECWJEmSGgzIkiRJUoMBWZIkSWowIEuSJEkNBmRJkiSpwYAsSZIkNRiQJUmSpAafpKdhtWDJUvY6+6IRufZ39tl1RK4rSZLGFmeQJUmSpAYDsiRJktRgQJYkSZIaDMiSJElSgwFZkiRJajAgS5IkSQ0GZEmSJKnBgCxJkiQ1GJAlSZKkBgOyJEmS1GBA7lERsV5EvDAiHtHleRMiYseI2LixbcuI2Lbxfa2I2Cwi/O9DkiSNOwag3rU1cB7wnC7PmwRcDrypse0k4EeN768BbgKmrUwHJUmSepEBuXctaHvvKCLWjIgpAJm5EEhgXuOQeW3fW+bXGec1ImKNle2wJElSL5g00h1QZxExEXhsh92b1/dNImKbDsfcnpl3A18B9omI5r6PR8TH266XbecvaXw+DThkSB2XJEnqYQbk0W1N4K+DHHPSAPtmAz8HPgz8L/AAZfb4r/X7F+txnwS2BF5Sv78C+ACwHRDAOsB93XZekiSpFxmQR7dW2cMxmfmR5o6I2A74E7BXZn63bd/3gW2BiwEy84+Nfa264vsz8+q6bQkwofH9tnrMjZn5wHD+IEmSpNHOgDyKZebiWvawdKjnRMR6wPOBT2Vme8kEwGLglcBVjW1vZfkb8r4F/AaY23WnJUmSepwBefRbQimLGJLMvCciNqBxA2ZEfIoSgpfTVpP8sO9t2z6SmccMtR+SJEm9yoA8+iWwOCK2p4TeRZQZ5UfX/Y/o5ya96zKzOfv7IHAbpSa5P1PrdRZ22N83UAcj4mDgYICZs2bxkolzBjp8lenr6xuR666MOXPm9GS/R5Jj1j3HrDuOV/ccs+45Zt1Z3eNlQB7F6tJskymh+AvA44G1gHspM8t3AcfWF8B6wN3AI9uamgosbtUY93Od64B7MvPJHfYvHqifmXk6cDrAFls9Ns9dMn2wn7ZKfGf27BG57sro6+tjdg/2eyQ5Zt1zzLrjeHXPMeueY9ad1T1eBuTRrZU052XmUyJibeBW4OjM/EzzwIh4JHA9cHpmzm9r53vAlQNcZy4D1xsfw+CraUiSJI0JBuTRbWZ9nwOQmfdHxDeBd0TEZzNzUePYdwPz6WfZt8z8OfDziHgd8KVOF+tnHWSAl2dmx3MkSZLGGp+kN7o9sb7f2dj2UeAxwBGtDRHxZOBQ4KTMvHWA9lqzxK+jlGu0XlcDv2vbdnjbOZIkSeOCM8ij2w71/Z+tDZn594j4JPChiPg98EfgLOBflAeCDKT1ZLwbm/XIEbEAmNu2rXXjX3OWWpIkacwzII9uzwDuB65r23408CzgXOBaYDNgt8wcbPmIIS8XJ0mSNF5ZYjFKRcRMSgi+MDOXNPfVm/D+i/Jwj22BXwJ/HkKzE4dw3TUjYhbLyjucQZYkSeOKM8ij1xsp/z5ntDZExCTKWsZvBP4T+Avw8/r9+oj4KvBd4NeZ+WA/ba7Z4VoTgNYTQXYAflU/LwT+tjI/QpIkqdc4gzwKRcSmwJHATcClEfGJiLiQsv7xBcAzgXcCO2bmYcATgG8CB9b990XEjRHxlramzwJmUWacmyZR1luG8ojpCyjrGj83M+9EkiRpHHEGeXS6E3gesHlm/rvOHO8IfIcShH/cXOItM68BDo6II4F9gX2A7SgPF6Fx3AJgQT/XW6O1PTMT2H24f5AkSVKvMCCPQpm5ELi8vqCscfz2zFw6yHn3Ap8DPhcRUcPuUK736MGPkiRJGh8MyD2gzvx2e44rVkiSJK0Aa5AlSZKkBgOyJEmS1GBAliRJkhoMyJIkSVKDAVmSJElqMCBLkiRJDS7zpmE1deIEvrPPriPdDUmSpBXmDLIkSZLUYECWJEmSGgzIkiRJUoMBWZIkSWowIEuSJEkNBmRJkiSpwYAsSZIkNRiQJUmSpAYDsiRJktTgk/Q0rBYsSV5x9t9Huhs94wUTF3DKahivs/bZepVfQ5KkscIZZEmSJKnBgCxJkiQ1GJAlSZKkBgOyJEmS1GBAliRJkhoMyJIkSVKDAVmSJElqMCBLkiRJDQZkSZIkqcGALEmSJDUYkEdARGwfEdOHqa0TIuINEbFRRBw6HG1KkiSNZ5NGugNjTUS8A3hvZm7SYf8E4A/AJ4F3D8MlX1nfFwKfiYh7M/Pr/Vx3TWDNfs5/IDMXRMRjgCkdrnFLZt43DH2VJEka9ZxBHn5LgcmtLxExMSKmRUTUTY+k/GHyw7ZjZqzg9RYDczPzTODzwEEdjnsHcEc/rxfX/ecDf+3wetEK9k2SJKnnGJCH31JKaG3ZHpgHLI2IBK6v238WEVm3LaaEVSJi3YhYGBH3RcS99XVf3bZ/REyPiMmNwL0UaH1+K/DiiJgREe3/d2ABsASYVV9PrtvnN/Z/PjOj9QKeV/fdvVIjIkmS1EMssVj1rgYeSwmiiyllFfsCT28cMw1Yo36eS5kFngvsB7ygfp8C/La29wiAZRmZoyLiqLbr7gL8pvF9CUBm3lnPbdVAL63vC/rp+9r1/Y5Bf6UkSdIYYUAeBhGxBfAzSqhdF9ggIq6ihN7XAdfUz4spYflPzdMpM/nXAWTmQuCM2u72wOzMPLtxrT0pYXcxJdxeSCnXOKEeMhlYC/hzWzeX1PNn1u/rte3PxjUeCzwOeFPddMMQhkGSJGlMMCAPj3spN93NB46hBOVPAFOBG4H3AW9vO+eWtu9PAv442IUy8w/N7xGxCLg7M68e7FRgIkObDd4c+AEwB/hAZlpiIUmSxg1rkIdBZt6bmZ8Evg5sTKk5PiMzPwPcBhxNKZF4Xt33yEad70TK7PJfI2KNfi/QEBHbRMS7Bti/TkQc3d8uYBGlJnp7SulGJz+rx6yfmccO1idJkqSxxBnk4fVyyphOAy6PiMModcRvaTvupkb9cNPPgdkR8SRKoJ0FTIyIJwLTMvNSYE/guIi4NDMvqedt3GjjZODVEXFnZp7W2D4BWJiZVwFExJx+rn9QRCy3Ckbt52JgrVr+8TARcTBwMMDMWbN4wcTr+jtM/ViHBatlvPr6/r3Kr7G6zJkzh76+vpHuRk9xzLrjeHXPMeueY9ad1T1ekZmDH6UhiYifAU8BHqSsdfwo4JmUG+D2Az5HuTnvinrKl4HNgBdSAvHkzLyv1i9v29b8fZm5bkRMpNQwL8zMHSPiJmAdYAvgVcBJwDeA12TmQ6tpRMS7gaMzc736fQtK3fOLMvPHEfH7+v2RwJ0sW6P5NGBmZrb3p19bbLV1Pu1/zh3KoQJeMPE6zl/y6FV+nbP22XqVX2N16evrY/bs2SPdjZ7imHXH8eqeY9Y9x6w7q2q8IuKyzNy5fbslFsMkInYAdgXOrJveT1ltYnNgB0pwPQ64ibK6xK7A2ygB+QzKHyuth3E8nXKz3bGUEo2ptR0ycwlwCLBPROxdz59BuVHvk8BngQOa4bhag7LU3DYRsQ2wZT8/4z5KecWWmXl1rWt+LPC7FRwWSZKknmNAHj5HA5cAVwFk5hXAVsA2lIdwXAZ8hFKusBOlDOMeypJvTwX+GBF7RkRk5pxmwM3MhZl5f+P7xZS1iT9VN11ECdWfzsyDM7O1dFvTesD6LHv4x4UdfsfPgG0iYpOI2BTYpP4uSZKkccGAPAwi4mmUEorjGtuCUg/8DeDblCXZ5gN31UO+T1l67Q/AG4C/ACcCGw3hekEpz5hLmWG+FDgHePYAN/o9AvhV4+bATv9f/2LgAUqt88spq1/8aLA+SZIkjRUG5OFxDWXVivNaG7IUd38U2C8zDwQOADal1AoDvIJyc91jgEsy82XAkzPz1v4uEBGTIqL14I6PALvXNlpPwnsnpWzii42n7DVtzRDWM6434n2XctPdq4GLM7N9STpJkqQxy1UshkFm3gW8tp/tV9ZHRG9PKa1IYM26e22WPaxjq4iYSn1YSJupEfHR2v6J9dHU7wfenJmXt7JwZt4YEe8BTgHuiIi31ZBORKxJuenv7H7a78/JlKf2Aew/xHMkSZLGBAPyqvcp4FmUlSyaN84dT1mXGMpNeFOBl0bET4DdgBdTVqVYC3gCcASlHvhE4AuZeXr7hTLz1Ih4FnA4MCsi3pSZD1DWPJ7M0G+2u5lSZjGDMustSZI0bhiQh99kGqUrmbl7c2dEzADuB/ZvlmS0HXMspVziZODUzLy9bm+1e+oA1z+Qsn7ylZQaZSg1zvMo9cUtW9X3VkifUq+xBqWeeQHlaXonRMQs4NjMXDDAdSVJksYEA/Lwm0IJyZ1Mq+9TBzjmQODmzFzuYR51dYpPAkTELpS65k0ogbt1zIKIeFFdDo6I2Bx4CfCtzJwXEZOBy4HHU24S/Fuj39OB8yjL0r2AsnrFHZSSjudExLM6rJAhSZI0ZhiQh99iyoNCOmkF42mdDqjrDw/mGuAgStnEGW3nL2l8vjEink8pmSAzF0XEV4EnA6dl5s310MmUWeYFwEvqUnIA74iIPwDXGI4lSdJ4YEAeZpl5PKW+uNP+mylPzVvZ69wRETMzc6Aw3jr2p23fP9bPMVtCWUKudXNfY99XVra/kiRJvcJl3nrYUMLxCrTps8clSdK4ZkCWJEmSGgzIkiRJUoMBWZIkSWowIEuSJEkNBmRJkiSpwYAsSZIkNbgOsobV1InBWftsPdLd6Bl9ff/mrNmOlyRJo4kzyJIkSVKDAVmSJElqMCBLkiRJDQZkSZIkqcGALEmSJDUYkCVJkqQGA7IkSZLUYECWJEmSGgzIkiRJUoNP0tOwWrwkOf2c21eqjYP33nCYeiNJktQ9Z5AlSZKkBgOyJEmS1GBAliRJkhoMyJIkSVKDAVmSJElqMCBLkiRJDQZkSZIkqcGALEmSJDUYkCVJkqQGA7IkSZLUYEDuARExLSLWH2D/IyLi8auzT5IkSWOVAXmUi4hJwIXA1yOi07/XfwEXDaGtGRExJSJiOPsoSZI0lhiQR7nMXAycBOwOHNPhsHnA/CE09ydgAbA0InKIr+uH5YdIkiT1iEkj3QENLjPPiogXAY+vs79TgKWZuageshRY0jwnImYAkzLznsbm5wCLgMX1nP6sDXwc2Bv4J/CRYfshkiRJPcCAPErV0oo1gHl1FvnNmbmg7vs0cFBELAWS8n8CIiIWt06v204DDmm1mZk3DHLNlwCnADOAdwKfboRwSZKkccGAPHo9k1pX3CoZru8nAZ8GzqbMBCdwMPAM4HWN86cA/x7KhSJiW8pM8cuAc4DDM/OWYfgNkiRJPceAPHr9FtiSUl+8CDgeeC3wucy8snlgRLwQeHJmXriC1/o2sBnw6sw8c8W7LEmS1PsMyKNUZs6j1AATEY8FXgmc2x6Oh8k84M+GY0mSJIjMHOk+aAB1abefUkoonpiZV0fEFOApwAPAQuBI4HnAC+pp04ApmXlpbWMmZfWKTvXEl1Bu2ntOP/smUmqh18vMf3To48GUMg9mzZq102dO/1q3P3M5M9cdP3+3zZkzh+nTp490N3qKY9Y9x6w7jlf3HLPuOWbdWVXjteuuu16WmTu3bx8/SaR3HQnMBr4K3BgRPwM+AXy/n2P/2vj8L0rZBMBVwEZDuNa8AfYtocN/L5l5OnA6wJZbbZ13L33iEC7V2b6zN1yp83tJX18fs2fPHulu9BTHrHuOWXccr+45Zt1zzLqzusfLdZBHsYjYE/hw/bobcBawMfAXYF1gMrBv45TTMjMoQfZxje07AutTlnCb0c/rd/XV3741anv+mStJksYFZ5BHqYh4PvB1SvnD9sA6wEuA92XmtfWYycCxjdP2j4j317WPH2xtzMxbB7nW0nrcnAEOWzLAPkmSpDHDGeRRKCLWpyy3dh2wF2U5txMoZRYfi4j966HvAR5PWQ7uJuA+4LjV3mFJkqQxxBnkUSgz746I1wCXZea9df3jxZR1ji8FvhURTwE+AHweuB/YHPgQ8IWI+ElmnjMinZckSepxziCPUpn53cy8qW3b0sw8iXLD3XeAaylPvGv5CvAL4GsRsftq66wkSdIYYkDuMRGxKXAhsBawT2Y2a40T2B+4FTgvIk6MiKlDaXaVdFaSJKkHGZB7QwBExJaU1SYeQwnHf6n7J7aOqY+IfjZwOWU95IX9NhixfkRsGRHPArbpdJwkSdJ4Yw1yb5gETM7MayPiMMpk8c/a97e+ZOaNEfHM+rnTk2AeDfy+8f0Xw9xnSZKknmRA7g1rUtYjJjO/18/+NVr7WzJz/kANZuZlEfFx4B7gt0DfsPRUkiSpxxmQe0BmrjHI/jcCb1yBdo9c4U5JkiSNUdYgS5IkSQ0GZEmSJKnBgCxJkiQ1GJAlSZKkBgOyJEmS1GBAliRJkhoMyJIkSVKD6yBrWE2aGBy894Yj3Q1JkqQV5gyyJEmS1GBAliRJkhoMyJIkSVKDAVmSJElqMCBLkiRJDQZkSZIkqcGALEmSJDUYkCVJkqQGA7IkSZLU4JP0NKyWLkl+duYdI92NUeG5B8wa6S5IkqQV4AyyJEmS1GBAliRJkhoMyJIkSVKDAVmSJElqMCBLkiRJDQZkSZIkqcGALEmSJDUYkCVJkqQGA7IkSZLUYECWJEmSGgzIPSAidomIx490PyRJksaDSSPdAQ3JmcAFwJtbGyJiJoP/+y3JzDsa58wAFgCLMjNXRUclSZJ6nQG5N8wH7m7bdhGw3SDnXQ88uvH9T8CjACJiqNe+ITO3GOrBkiRJvc4Si96wlDLz2zQP+HJmRn8v4Cxgbts5zwEeAWwEzOrw2hI4px7/T+CDq+IHSZIkjVbOII9SEfFx4En16xbAayPimfX7VymhmYh4BPDzttOfXt+XK6PIzBsGueZLgFOAGcA7gU9n5qIV/AmSJEk9yYA8em0HLAGOBr4B/Aw4Hfg+ZRYYgMz8F7BV+8ldlFAQEdsCHwFeRpk9Pjwzb1mJvkuSJPUsA/LoNR9YkJm/j4j5wL/r54XAQmBi68C6f2rj3E3q+0SG5tvAZsCrM/PMle+6JElS7zIgj15LBtiXwN+Av9fvawEPTRln5uKI+Bew4RCvNQ/4s+FYkiQJwtW+RqeI+DawODP3j4irgG9n5gci4nrg5Mz8v4gYyjJvWZeEWwB0qie+hFLT/Jx+9k0E1gDWy8x/dOjrwcDBALNmzdrp86d+bbCfNy7MWH/wvz/nzJnD9OnTV0Nvxg7HrHuOWXccr+45Zt1zzLqzqsZr1113vSwzd27f7gxyb5vHwP+G+1NWs7iKsnLFUNrrZEmna2Xm6ZT6aB671da51rwdhnCpsW/27FmDHtPX18fs2bNXfWfGEMese45Zdxyv7jlm3XPMurO6x8tl3nrbPODQfpZ4a6193FoabkdgfWBtygoV7a/f1Vd/+9agBGP/zJUkSeOCM8i9bRFwQkR8rG176w+fhQCZeetAjUTE0nrcnAEOG6gmWpIkacxwBnn06rRO20TqGsiUm/WOyMx1my/giXX/0v4akCRJUmcG5NFrWtv3NSLiNMpybHeOQH8kSZLGBQPy6DW17fs84CmUm+6+2dh+QkTc23wBV66mPkqSJI051iCPUpm5Wz+bn5mZc9u2HZGZpzY3RMQWwHVdXC5oeyy1JEnSeGVA7g0TgQn9hOMJDHyTXsfnTUfE+sB6wKbANsAVw9RXSZKknmZA7g2TgSn9bJ/KwDPIkwdo89HA7xvff7GSfZQkSRoTDMi9YRoPv2kPHl6n3G6NTjsy87KI+DhwD/BboG+FeydJkjSGGJB7QGZu1mF7pyfbXc8A5RWN445cuZ5JkiSNPa5iIUmSJDUYkCVJkqQGA7IkSZLUYECWJEmSGgzIkiRJUoMBWZIkSWowIEuSJEkNroOsYTVhYvDcA2aNdDckSZJWmDPIkiRJUoMBWZIkSWowIEuSJEkNBmRJkiSpwYAsSZIkNRiQJUmSpAYDsiRJktRgQJYkSZIaDMiSJElSg0/S07DKxck/Tr5tpLsxoh57+EYj3QVJkrQSnEGWJEmSGgzIkiRJUoMBWZIkSWowIEuSJEkNBmRJkiSpwYAsSZIkNRiQJUmSpAYDsiRJktRgQJYkSZIaDMiSJElSgwF5FYqInUe6D+2imDHS/ZAkSRqtJo10B8aqiHg+8JOIOCYzP9LluWsBj1zJLvwrMx/oZ/vawL0RcWJmvnMlryFJkjTmjOuAHBGbAx8AFgALgRzsFGAKMA04IjPv6dDuhsCXgeuBsyJi434Oy8y8rcN1dgR+MUhfBvNC4PxGn9YBFlECMsDVEfHQv39mLo6IRwFP6aetf2fmr1ayP5IkST1hXAdkYD3gNZSAvIDBAzKUMVsDOAZ4WECOiOnAD4BN6qZ/dGjnE8ARHfa12n1nZp44hD41r/964IvAvW27fg7s0Ph+an21znsssDPwdeDvLBuLR1F+jwFZkiSNC+M6IGfmFcDk4WqvzhSfAzyeMoP727ZDZgC/pMwsHzlAU/MHuMZelNrxH2XmvMb2Q4CzGocubDv1VZQZ8N2BE4DdgBuAfYCPAfc3rrtDZs6v7f6G8seDJEnSuOBNesMkIvYALgO2BGZn5vmZeW/zBXyUUqLxisxcPEBzS5c1G0dHxIca+w6mlG+s37j2OsCnga82jltuNjwz/5KZf6bMbC8CLsnMa4C76iFzgSUd+tNpuyRJ0phjQB4GEXEA8EPgVmAmcFlEZPsLeDWwEfDvtn1v79B0UspA3hcRj6s1w88AvgYcHBHfqsc9m/Jv+dlB+hnAvsCvM7M1wzytvi+g8/9RGNf/p0GSJI0vBuRhkJlnAodQgupiymzu4xuv19VDD2jbPrtun0dnxwDXUWqWd6GUaZxBmQXeNyKeAzwfuJsS0gfyRuDRwHca2zYFlmbmIsrsNsBGEbFxLRmZ3NguSZI05jkzyEMlCvMzs2OtbURMAKYCEzNzTvv+zDytHpfAnZl5dePc1ioWN7Ztb7WzqNN1M3N+RLwNOA/YkFI3/AvgN8D+wP9Syi3OycxFZZK43/6/DjgZuAb4bETsCryBMqP8t3pYKwhf33b6TZ36V9s+mFL6waxZs/jnRn8a6PAx7199fx3ysXPmzKGvr2/VdWYMcsy655h1x/HqnmPWPcesO6t7vMZ9QK5lC/fWz0M55bfA0wfYPwmYGRHbNLZt3npv275RfR9w9YzM/HFE/AjYA/hoZiawKCKOBr5bD3tjp/Mj4nDgJEoJyEsz88GI+DOwd/09p9VVLM6irFjRrmOAr/07HTgdYOstt87H3Lb9QIePeY/db6PBD6r6+vqYPXv2quvMGOSYdc8x647j1T3HrHuOWXdW93iN+4BMuSHuy5QVHDquHsGyNZD/3fGAiCnAROAt9dXuzBXvJhdSAvJDq1Nk5vci4ibKzHL7ihlNpwCPBY7PzBvrubdHxMw6Q/1j4C2Z+SwevvqFJEnSuDLuA3JmLgVeP0zNrVvfD8zML7Y2RsRs4CLgWZn5i8b2zRikfKEeN5WyZvJ84L0R8ZXMvC4idmPZE/cOBj7VoYlNKWsjr9U2g92aNZ8ITGjf1zqEUoc8ITP/OFhfJUmSet24D8jDbLP6fk/zKXWUAAowscP2wbwN2JhyM96PgRMjYj/KjXt/pizRdkxEfKnD+W8Gjh7CdQYqnr0B2GKI/ZUkSepZBuThtW19/06H/X3dNhgRm1DC7Rczsy8iPg1cTVlTeTtK2cVESu3w+1l2w13Tx+trTmYuqe1OpJR8vIyySsYCSk30dzLzofKQetwawPRu+y5JktSLDMjD6+mUG/6e0rb9aZSHeOxPeZhIy8bAJQO0F5Sb3wL4L4DMfFdEvBZ4F3BmZp4HEBEXUJaYe5jMvG+5RiM2AL5CWWZuL8oNftMpS9WdHxEbAW/KzHtqoJ5TX5IkSWPeuF0HOSLWjog16wxpt+dOioiZdXb3oW2UVSEuysxrmi+W3dj3r7bt1w9yqVmUpeWOzMxb63WC8rS+vwOHtw7MzN0z8/8N0u+pEXEY8Bdga+DpmfnjRhu/BZ4F7ABcHxHHRcSGg4+IJEnS2DFuAzLwK+BBYHF/T70b6EVZ9uwO4OuN9g6hzAif0c+11qzv7evITWs/sO2424EXtNZYBsjiv4Ht6+Or2814WGMRT46IYymlFCfWPj45Mx+2YHHdtiPwecos9b8j4tcR8b4OfZUkSRpTxnOJxXcp5Q2LKEu9daO1ssONABHxBODDlJnZ7/VzfKt+d3I9/qmUh3Q8rW6/v+34VqCmrnn8MI1HRVPbPIpSMtFao7nZ5ksodczfBI7NzPY65Yk0bhjMzAeBIyLik8BhwGspayhLkiSNeeM2IGfmMcPY3C6U2eB31GXj2q1V39eo71dTHj+9BvAHHn7z3gYr0IffAm+lPGHvvMz8Z2Pfhyn1ytd2OHdNGqG8JTNvoCwrdxTd/xEhSZLUk8ZtQB5Omfn5iLioLZQ2938B+ELj+/0R8Rzg9hpC24+/mIeXYwzWh58Cm3TYtxToFI7JzBcM0vaSbvoiSZLUywzIw6RTOB7g+N+tqr5IkiRpxY3nm/QkSZKkhzEgS5IkSQ0GZEmSJKnBgCxJkiQ1GJAlSZKkBgOyJEmS1OAybxpWMSl47OEbjXQ3JEmSVpgzyJIkSVKDAVmSJElqMCBLkiRJDQZkSZIkqcGALEmSJDUYkCVJkqQGA7IkSZLUYECWJEmSGgzIkiRJUoNP0tOwysXJrSf8eaS70TMWP2K+49WlXhuzjY/YdqS7IEnqkjPIkiRJUoMBWZIkSWowIEuSJEkNBmRJkiSpwYAsSZIkNRiQJUmSpAYDsiRJktRgQJYkSZIaDMiSJElSgwFZkiRJajAg97iImLyC560VETOGuz+SJEm9zoDcIyJiaj/b1gDmRsTPV6DJZwG3RMTbV7pzkiRJY8ikke6ABhcRhwFHRsTbM/N7zV2Uf8PrBjl/I2BTYAGwtG7eE1gLuDkithnodGAq8EBmXruCP0GSJKlnGJB7wxXAXOC7EfF94NDM/Dcwse6/eZDz9wJO6bDv20Psw5eB1w/xWEmSpJ5liUUPyMxfAjsCHwVeAlwRERuy7A+cewZp4mvALGBqZgawb93+wsyMuu1lwIeAJ7S21e2TgPWAdw/nb5IkSRqtnEEexSJiCjAtM+/PzIXA0RFxMbBFZt4eERvXQ+cP1E5m3t+26R3A9cC1ETEjMx8AHgf8P+CnwF/r9V8P/DMzLx6mnyRJkjTqOYM8un0auCYiDo2IiQCZeX5mnlb3r1nfHxhqgxHxn8AzgY8AJ1FC8rqUWWKAKfW4Der+D0VErOwPkSRJ6hUG5NHte8C9wGeAP0TELm37167vg5VYABARM4ETgMspN/a9EPh8Zt4L7FwPa5VfnAhMA96cmbli3ZckSeo9YfYZ3WqZxfuBo4Hbgcdl5oN132zgIuAZmfmrQdqZBFwAPAd4NvAW4PnAlsBmwB8pNwPuCOwGPAPYNDPfMoQ+HgwcDDBr5qydvnbyV7r7kePYvCkLWWPhlJHuRk/ptTGbtNG0ke4Cc+bMYfr06SPdjZ7heHXPMeueY9adVTVeu+6662WZuXP7dmuQR7lae3xsRJwPLGmF42rD+n77QG1ExDrAmcBs4IOZ+QvgFxGxJTAd+BZwB/Bc4JP/v707j5KrLPM4/v2RnRgJS0gAQbbEjJgAChoYIi2bgssg4iAiqGGQQdl0xGE7g7gALqgYRCaCGkCYw4HjFkFka5ZxAUUJUcKIyhZZhRBjNkKe+eN9i9wU1d1101VdXV2/zzl1uu6tu7zvk5uuJ2+e+15gHnAs6abAeto4B5gDMGWHKfG6RVvX1zljwVaP4niV024xm3TYTq1uAt3d3XR1dbW6GW3D8SrPMSvPMStnoOPlBHmQkvQl0jzFLwBRWH9EYbNp+ecnJRVv1NsAGEEqkTga+BzwduBS4NP5ONsDh5Nmp1gO7B8RSyT9G7AUuDwf9wqgG3gwIp5vbC/NzMzMBh8nyIPXIcBWwKr8qmUTUgJ9aNX6YaQ/2zHAMcDHSbNWfBV4l6Rv5GOvAK4A/isiHgeIiBeBEyTNJY0mfzkf8zPAWY3omJmZmdlg5gR5kIqIHXr7XNJ0Ut3wmRHxxToOeb6k1wAPkqZx+zaplGIJsFEuwyhaShp9ngJsD/ygTPvNzMzM2pUT5DYkaQTpyXhPA9+QtCvwh4hY2ceuC1j7Z74fad7jvtweEXuvd2PNzMzM2oyneWszeVaLK4A3AUeSpnq7FbhP0r597L4SuKDwlLyfAD8pLD8MfKGwvAhw3bGZmZl1FCfIbUTSFOA2Un3ysRHxs1w7fCRpZPgmSXPzgz9q6fWJez3oqf7ZzMzMbEhygtwGJG0i6QvAfGA74MCIuLTyeUT8GJgOXAIcBSyQdEBLGmtmZmbW5lyDPEhJegWwB2mGig8Ao4C5wCkR8Wz19hGxFDgmz5d8CXCDpFnAdcCOpFKJYcDGkqbm3cbmc1WWRwCbFJaHA+PyDYHjgQci4slG99XMzMxsMHGCPHgNB04FZgJXA+dExB/62ikirpH0a9J8x1eRbsb7cWGTo/Kr6P7C+2Pyq+KA/AJ4J2nmCzMzM7MhywnyIBURiyUdCmwYEYtK7vsQ8CEASXcCrwGeAFbkJ/PVRdIwYCRppHlTwKPHZmZmNuQ5QR7EIuI54Ll+HmMxsHg9932R9JS95cAz/WmHmZmZWbvwTXpmZmZmZgVOkM3MzMzMCpwgm5mZmZkVOEE2MzMzMytwgmxmZmZmVuAE2czMzMyswNO8WUNpuJj0iZ1a3Yy2sbD7aSYd5niV4ZiZmVmzeQTZzMzMzKzACbKZmZmZWYETZDMzMzOzAifIZmZmZmYFTpDNzMzMzAqcIJuZmZmZFThBNjMzMzMrcIJsZmZmZlbgBNnMzMzMrMBP0rOGitVreGr2za1uRttYPXGZ41WSY1Zef2K2+Qn7Nrg1ZmaDn0eQzczMzMwKnCCbmZmZmRU4QTYzMzMzK3CCbGZmZmZW4ATZzMzMzKzACbKZmZmZWYETZDMzMzOzAifIZmZmZmYFTpDNzMzMzAqcIJuZmZmZFQyaBFnSVyR9WNJEScf181jTJL2ixPZbStpO0oA8eruRfc3HK9VfMzMzM+vZoEmQgcOBacB+wEWSDq+1kaSTJT3e00EkbQDcA3y6xLk/AvwZ2L/EPv1RV1+haf1F0jmSugrLr5P0BUm75OWdJM2TNKPMcc3MzMza3WBKkFcDyyLie8ClwNE9bLcGGFFZkDRM0mhJyqu2BoYDP6naZlwv5944//zF+ja+pHr7Ck3ob056TwU+UTjOGOBTwMy8vAp4M3CVpLFlOmdmZmbWzppWUiBpPPAUsByIympSInYUMA9YCayOiCAlgpVk7QRgTU7ylkfE6sKh15ASzIppwG/zOYtNuKVqeSUwuofmbgn8A3hH1T49GZ77cWlErGpiX6HB/ZU0BvhObu8Hc3vIx3yWNKo9OyL+KOlDpAT8/ZI2i4hzew+LmZmZWftrZs3tMtLI6DLgvcBb8/JI4FfAQmArWCfRO13S6VXH2QP4ZS/nWQhMBlaQEslPAocCxdKA0aRktSdb5c8v6aNPkBLbkfn9VaSR1oHqK/Sjv7kc43vAVOC2iHiuapNbgIMkfQN4PbBzPs5DwG8lDYuIF/ton5mZmVlba1qCHBGrgMsh3UQGdEXEtZXPJb0LeJGU5K0BbiKVCXwlbzICGAv8XtK2pORtGTAe2FTSAlLy9kHgwfx+NSl5vK/QFJFKSf7SS3O3A66OiB5rgYtyWcKoiFjR6L7m7RveX0kjSeUcBwOP53XTgTOBKaSkeVTe/GBgDqmu+e6IeLaeuJiZmZkNBQMya0MtEXFPcVnSC8CzEbGweltJw4ALSKOmZ5ISx6+SErpHSPW0J1XtVn1j267A72ocewwwCXi4RNsjt6Xe7evua7aYxvf3SOD9wPGk2uJJwDPAROBnwOeAu4BzSCPSV0bEH+vsopmZmdmQ0bKb9CRNlfQfvXy+kaQzACJicURcQCppmESq9b08Ii4CngTOIJUz7Js/2zoiFBEChpFGW+/PyXC1HfPPuhPkssr0FZrT34i4FNg5H0N53V8jYu+I+BTwg4h4BDiFVI99WWV/ScdLOlHSKxsWFDMzM7NBqqkjyJJ2JSVjE4Bh+b/0R0fEXcC7gHMl3RURd+RdJhV2vxD4gKRnIuK/87qDc5tHk2piP0qq+f1Y1akf7eFmu9uArqp1U/LPkDS1RPeWRMRfKwtN6Cs0uL8RsSC/HZdflbbvDlwr6f0RcWe+Oe/7wM2SjidNS7cncB2wpPokkj5CmiqPCRMmMH/islptsRqWj1jjeJXkmJXXn5gN7+5ubGPawNKlS+nuwH73h2NWnmNWzkDHS2snMWjCwVPd7E5Vq5+PiPG5bOI+YFVE7CLpUWAjYFtSKcBs4H+AIyszO0i6BdidNMJ5D/BqYC/SjA3vJd1kNwO4N59rLvAq4G2k5HVERDxf1cbTSGUFZV0QESc3q6/N6m8+7i+AiRGxfV6eCPwBeBTYNSJC0rtJs11slHebFxHv7CsoU3aYHHeefHFfm1k2f+Iypj+5Yaub0VYcs/L6E7PNT9i3wa0Z/Lq7u+nq6mp1M9qKY1aeY1ZOs+Il6TcRsVv1+maXWMwg3YB2Nqk0YBSwDUCeDeHfgfdIOoSU2I0j3bx2AfAt4IhCcrwz8BbSLAwAp5FmhtiGNNvCbOBcUpL3y7ztifm4l5P+MfCyZDEizq2UJ9TzyucCqK7PbVhfm9FfSTtL2kfSG4GTgffl0o+ppHmgTwU+Abwmr7sfOJBU+3wFcH7JEXYzMzOzttTUEouIWAprpzbLsz2sKnx+u6SNga/nVbeSEr3ZEXFi1eHOAO4AFgAHR8S9knYk1eF+C/gN8HnSbBBvIJUlPEe64eyHwO9yHfCPC3P/ro+t8891ZolocF+b0d+TgMOAF0gzalRisGn++WxhXbXh+bi3Agf1sI2ZmZnZkNCyWSzgpenS5pJmaXiSNIvCc8CbJY2JiOV5uzeRSgoOAnYo7HshKen7Dqn8oDizxI8K7/cjja5+LZ/jCUmT8z7rJLK1mkkaDR4dET9lbYJc6qa+evvarP5GxCxgVlWbPg+cTpr+7WOkad1ujYif5c9HAHtGxG1l+mpmZmbWzgY8QZY0HNgwIpaQRkAPID0g4/t5k4+T5gP+jqTD8+jng6RZHK7PN42R62TPAa6JiGskTWLtQzweIiWStwEbAosi4mZJ4yNicT7PEcBZZZtPLpugjgR5PftKk/pbbNcw4LOkso35wHGkOZX/GThM0vQ8In4O6XHUR0fEd+sNkpmZmVk7G8gEeVRO8I4CviYpSAnasRHx20JpwiOSTgG+CTwt6cSI+Fvebx0RMV/SqvxwjtWkEoHKnSivJNXWAuwoaRTrlkXMBW4mjR6v7KPtwwrH3ZY0h/HSJvU1mtRfACTNAM4nJerLgEcj4oX82SxS6cY8STeSntL3ReCyPuJjZmZmNmQ0LUHOJQH7AW8nzdQwFngt6UawLUj//f/tiJhTvW9EXCxpJumhFhMkHRMRf+/hVF8HZpKS3NWF9eeT6m0hlUiMAt4J3JDP8Rd6f7peT3YkPazjJQPYV1jP/uabA48n1T0/RHoc9mer+rACeA9pOre9gfMj4j97C4aZmZnZUNPMR02HpLNJNbQXAhdHxFMAkiqzZ/Q2H9gs0pzC80kjnRUjKMy+EREHFHeSNI40V+/7IuL6/vaj6thTgWnAOsdtYl+hcf1dBrwO+BTpxsAVkj4LDJd0KGmE+9GIOFjSAcA1wMclTQGuBG6KiGd6Ob6ZmZnZkNDsEotZwGPV5QgRsYY0vRmS9iDVA29B4SEUEbFS0oF5irSikaSksSej889R/Ww7uX1HAueRYrUZqe53bo1Nm9FXaFB/I+KnkraJiBW5LZsCk4FNSKPJNwMX5W27Je2U+/0B0kg0kk6JiC/3dh4zMzOzdtfUeZAjYmEftbqQbkg7GribNH9vcf9aCeNq0oMzelJJFEf3sk0ZVwNPk0Z3LwT2iogfVm/UpL5CA/tbSY6z8aRHUs8D3hAR+1Vmr8jbPhkRHybVXJ9GmuLtkr7OYWZmZtbuWjrNG0BEPC1ps4joLQksbn8+qd62p88fI43yNqp9K4FdGnSsUn3N+zSlvxHxJ0nbRcSTfWy3iDSSfF7Zc5iZmZm1o2Y/Sa8uZRLGdjeY+tpXcmxmZmbWiQZFgmxmZmZmNlg4QTYzMzMzK3CCbGZmZmZW4ATZzMzMzKzACbKZmZmZWYETZDMzMzOzgpbPg2xDi4ZvwOYn7NvqZrSN4d3dbP6vXa1uRltxzMpzzMzMyvEIspmZmZlZgRNkMzMzM7MCJ8hmZmZmZgVOkM3MzMzMChQRrW6DDSGS/g480Op2tJHNgGda3Yg245iV55iV43iV55iV55iV06x4vToiJlSv9CwW1mgPRMRurW5Eu5D0a8erHMesPMesHMerPMesPMesnIGOl0sszMzMzMwKnCCbmZmZmRU4QbZGm9PqBrQZx6s8x6w8x6wcx6s8x6w8x6ycAY2Xb9IzMzMzMyvwCLKZmZmZWYETZDMzMzOzAifI1jCSLpX0c0lntrotg4GkjSRdL+lGSd+XNFLSI5K682ta3u5sSXdLurCw78vWDXWShlfHp97YdGK8ACQdV4jX7/LfQV9jPZA0UdId+f0ISfPy76xZ/V03FFXFa5t8Td0iaY6SrSQ9VrjeJuRtX/Zd0CnfD1Ux61d8OjRmZxfitVDSaa26zpwgW0NIOgQYFhF7AltKmtzqNg0CRwBfiYj9gSeAU4GrIqIrv+6TtBuwF/BG4DFJ+9Va16oODLDpFOIDjKKO2HRwvIiIbxbidQdwEb7GapK0MTAXGJtXnQD8Ov/Oeoekcf1cN6TUiNexwHERsQ+wNTANeBPw+cL19nSt74JO+X6oEbP1jk+nxiwizir8TrsPuIwWXWdOkK1RuoCr8/tbSF++HS0iLoqIG/PiBGA18G5Jd0r6nqThwJuBayPdLXsTMLOHdZ1gBoX4APtQX2w6NV4vkbQVMJH0ReJrrLYXgcOAJXm5i7W/s34O7NbPdUPNOvGKiDMi4v782aakJ5rNAD4q6ReSvpo/6+Ll3wW11g1F1ddYf+JTa91QVB0zACTtDiyKiEW06DpzgmyNMhZYlN8vIX1ZGyBpD2Bj4EZg74jYC1gMHETtuHVqLO9m3fiMob7YdGq8ij4GfJOXx9DXWBYRSyLi+cKqeuPSkfGrES8AJB0G/D4i/gpcD+wZEXsAUyRNp0PjBTVj1p/4dGrMKk4CZuf3LbnO/Khpa5SlpIQG4BX4H18ASNqE9Jf8PcATEbEyf7QQmEztuHVqLOdXxWck9cWmU+MFgKQNgLdExOmSRvkaq1slBs+TYrC0n+uGPEnbA58EKiU5P/f11qv+xKdTY4ak8cDmEfGnvKol11nHBNya7jes/e+MnYGHWteUwUHSSNJ/95wWEQ8Dl0vaWdIw4N3AvdSOW6fGsjo+Y6kvNp0ar4qZwK/ye19j9as3Lo4fL9WKXgXMKoz43SBpC0kbAm8FFuB4FfUnPp0aM4B/Aa4rLLfkOvMIsjXKD4A7JG0JHEiqGep0RwNvAM6QdAZwK3A5IOBHEXFTHv07V9IFwNvy6+Ea6zrBZ4AryfEBPke6pvqKTafGq+KtwO35/Tox9DXWq7nAdZJmAq8l/SNjUT/WDXWnAtsAsyUBnAWcTfq9tgq4OCIekPQ4L/8uiBrrOkF/4tOpMYP0O+3LheWWXGd+kp41TB5h2B+4PSKeaHV72oWkMcDbgXsi4s89retE9cbG8eqdY1Zb/iLdC7ihMiran3WW1Pou8PfDWvXGxzHrXbNj5gTZzMzMzKzANchmZmZmZgVOkM3MzMzMCpwgm5mZmZkVOEE2M7O2J2kXSbu0uh1mNjQ4QTYzs6Fgl/wyM+s3z2JhZmYDTtJo4LvAq0iPxT4CuBjYEngM+DBwOtAdEd2SPpR33RYYQZpibSPSHM4nkR6MArAoIvYdiD6Y2dDlEWQzM2uFjwD3RsRewLWkJHdBROwN/B8wq5d9d8zbXQnsExGnAecB5zk5NrNGcIJsZmatMBW4K7//LrAFa59I9yvgn6q2H1N4f1n++RQwskntM7MO5gTZzMxaYSGwe35/el6uPBp2BvB70qNlx+V1xcdh/6PG8ZYDGwIoPwvZzGx9OUE2M7NWmAO8XlI38HrSKPJOkm4HJuflHwGnSLoY+Fsfx7sROETS/wIzm9RmM+sQvknPzMzMzKzAI8hmZmZmZgVOkM3MzMzMCpwgm5mZmZkVOEE2MzMzMytwgmxmZmZmVuAE2czMzMys4P8BSP2cLGBsrxoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,15))\n",
    "sns.countplot(y='产品名称', data=order, ax=ax)\n",
    "plt.grid()\n",
    "plt.yticks(fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 订单数据都是棒球运动的相关商品\n",
    "2. 卖的最多的是棒球手套"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数值型数据\n",
    "产品成本\t利润\t单价\t销售金额"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    60391.000000\n",
       "mean       370.168304\n",
       "std        508.012963\n",
       "min          4.000000\n",
       "25%         29.000000\n",
       "50%        160.000000\n",
       "75%        500.000000\n",
       "max       1999.000000\n",
       "Name: 产品成本, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order['产品成本'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='产品成本'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(order['产品成本'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "大部分产品的成本都在500元以下，最少4元，最多1999元"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 利润"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    60391.000000\n",
       "mean       271.375418\n",
       "std        398.134438\n",
       "min          3.800000\n",
       "25%         28.000000\n",
       "50%         59.900000\n",
       "75%        349.000000\n",
       "max       1200.000000\n",
       "Name: 利润, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order['利润'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='利润'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(order['利润'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "大部分订单的利润都在200元以下，中位数是59.9元，最多1200元"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 单价"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    60391.000000\n",
       "mean       641.543722\n",
       "std        867.702552\n",
       "min          7.800000\n",
       "25%         59.000000\n",
       "50%        219.900000\n",
       "75%       1099.000000\n",
       "max       3199.000000\n",
       "Name: 单价, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order['单价'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='单价'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(order['单价'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 销售金额"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    60391.000000\n",
       "mean       641.543722\n",
       "std        867.702552\n",
       "min          7.800000\n",
       "25%         59.000000\n",
       "50%        219.900000\n",
       "75%       1099.000000\n",
       "max       3199.000000\n",
       "Name: 销售金额, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order['销售金额'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='销售金额'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(order['销售金额'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "大部分订单的销售金额都在1000元以下，最多3199元"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 每条数据的物品数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1597: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.obj[key] = value\n",
      "D:\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1676: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._setitem_single_column(ilocs[0], value, pi)\n"
     ]
    }
   ],
   "source": [
    "order.loc[:, 'count'] = order['销售金额']/order['单价']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    60391\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order['count'].value_counts()  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**一条数据只是记录了一条订单的一个物品相关信息**\n",
    "\n",
    "**时间数据精确到天，那么假定同一天内同一个用户的相关数据为同一条订单**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 商品区域销售情况分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>产品成本</th>\n",
       "      <th>利润</th>\n",
       "      <th>销售金额</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th>销售大区</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">中国</th>\n",
       "      <th>东北区</th>\n",
       "      <td>11436.98</td>\n",
       "      <td>11204.49</td>\n",
       "      <td>22641.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>东南区</th>\n",
       "      <td>13530.95</td>\n",
       "      <td>10199.49</td>\n",
       "      <td>23730.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中国台湾</th>\n",
       "      <td>2495383.84</td>\n",
       "      <td>1725374.38</td>\n",
       "      <td>4220758.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中国澳门</th>\n",
       "      <td>4428766.65</td>\n",
       "      <td>3078036.66</td>\n",
       "      <td>7506803.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中国香港</th>\n",
       "      <td>1960151.06</td>\n",
       "      <td>1355867.68</td>\n",
       "      <td>3316018.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中部</th>\n",
       "      <td>9609.99</td>\n",
       "      <td>7313.60</td>\n",
       "      <td>16923.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>西北区</th>\n",
       "      <td>3470886.90</td>\n",
       "      <td>2723162.54</td>\n",
       "      <td>6194049.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>西南区</th>\n",
       "      <td>4497450.93</td>\n",
       "      <td>3378238.69</td>\n",
       "      <td>7875689.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新加坡</th>\n",
       "      <th>新加坡</th>\n",
       "      <td>2121875.14</td>\n",
       "      <td>1435482.98</td>\n",
       "      <td>3557358.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>韩国</th>\n",
       "      <th>韩国</th>\n",
       "      <td>3345741.58</td>\n",
       "      <td>2663752.37</td>\n",
       "      <td>6009493.95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   产品成本         利润       销售金额\n",
       "country 销售大区                                 \n",
       "中国      东北区    11436.98   11204.49   22641.47\n",
       "        东南区    13530.95   10199.49   23730.44\n",
       "        中国台湾 2495383.84 1725374.38 4220758.22\n",
       "        中国澳门 4428766.65 3078036.66 7506803.31\n",
       "        中国香港 1960151.06 1355867.68 3316018.74\n",
       "        中部      9609.99    7313.60   16923.59\n",
       "        西北区  3470886.90 2723162.54 6194049.44\n",
       "        西南区  4497450.93 3378238.69 7875689.62\n",
       "新加坡     新加坡  2121875.14 1435482.98 3557358.12\n",
       "韩国      韩国   3345741.58 2663752.37 6009493.95"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.float_format', lambda x: '%.2f'% x) \n",
    "order.pivot_table(values=['产品成本','利润','销售金额'], index=['country', '销售大区'], aggfunc=(np.sum))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "销售大区的东北区、东南区和中部销售金额较少"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>产品成本</th>\n",
       "      <th>利润</th>\n",
       "      <th>销售金额</th>\n",
       "      <th>销售利润率</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th>销售大区</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">中国</th>\n",
       "      <th>东北区</th>\n",
       "      <td>11436.98</td>\n",
       "      <td>11204.49</td>\n",
       "      <td>22641.47</td>\n",
       "      <td>0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>东南区</th>\n",
       "      <td>13530.95</td>\n",
       "      <td>10199.49</td>\n",
       "      <td>23730.44</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中国台湾</th>\n",
       "      <td>2495383.84</td>\n",
       "      <td>1725374.38</td>\n",
       "      <td>4220758.22</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中国澳门</th>\n",
       "      <td>4428766.65</td>\n",
       "      <td>3078036.66</td>\n",
       "      <td>7506803.31</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中国香港</th>\n",
       "      <td>1960151.06</td>\n",
       "      <td>1355867.68</td>\n",
       "      <td>3316018.74</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中部</th>\n",
       "      <td>9609.99</td>\n",
       "      <td>7313.60</td>\n",
       "      <td>16923.59</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>西北区</th>\n",
       "      <td>3470886.90</td>\n",
       "      <td>2723162.54</td>\n",
       "      <td>6194049.44</td>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>西南区</th>\n",
       "      <td>4497450.93</td>\n",
       "      <td>3378238.69</td>\n",
       "      <td>7875689.62</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新加坡</th>\n",
       "      <th>新加坡</th>\n",
       "      <td>2121875.14</td>\n",
       "      <td>1435482.98</td>\n",
       "      <td>3557358.12</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>韩国</th>\n",
       "      <th>韩国</th>\n",
       "      <td>3345741.58</td>\n",
       "      <td>2663752.37</td>\n",
       "      <td>6009493.95</td>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   产品成本         利润       销售金额  销售利润率\n",
       "country 销售大区                                        \n",
       "中国      东北区    11436.98   11204.49   22641.47   0.49\n",
       "        东南区    13530.95   10199.49   23730.44   0.43\n",
       "        中国台湾 2495383.84 1725374.38 4220758.22   0.41\n",
       "        中国澳门 4428766.65 3078036.66 7506803.31   0.41\n",
       "        中国香港 1960151.06 1355867.68 3316018.74   0.41\n",
       "        中部      9609.99    7313.60   16923.59   0.43\n",
       "        西北区  3470886.90 2723162.54 6194049.44   0.44\n",
       "        西南区  4497450.93 3378238.69 7875689.62   0.43\n",
       "新加坡     新加坡  2121875.14 1435482.98 3557358.12   0.40\n",
       "韩国      韩国   3345741.58 2663752.37 6009493.95   0.44"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "region_ = order.pivot_table(values=['产品成本','利润','销售金额'], index=['country', '销售大区'], aggfunc=(np.sum))\n",
    "region_.loc[:, '销售利润率'] = region_['利润']/region_['销售金额']\n",
    "region_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "销售利润率方面除东北区外差不多，都在40%到44%之间，利润率最高的是东北区49%，但是销售金额少。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>产品成本</th>\n",
       "      <th>利润</th>\n",
       "      <th>销售金额</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th>销售大区</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">中国</th>\n",
       "      <th>东北区</th>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>0.0006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>东南区</th>\n",
       "      <td>0.0006</td>\n",
       "      <td>0.0006</td>\n",
       "      <td>0.0006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中国台湾</th>\n",
       "      <td>0.1116</td>\n",
       "      <td>0.1053</td>\n",
       "      <td>0.1089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中国澳门</th>\n",
       "      <td>0.1981</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.1938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中国香港</th>\n",
       "      <td>0.0877</td>\n",
       "      <td>0.0827</td>\n",
       "      <td>0.0856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中部</th>\n",
       "      <td>0.0004</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>0.0004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>西北区</th>\n",
       "      <td>0.1553</td>\n",
       "      <td>0.1662</td>\n",
       "      <td>0.1599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>西南区</th>\n",
       "      <td>0.2012</td>\n",
       "      <td>0.2061</td>\n",
       "      <td>0.2033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新加坡</th>\n",
       "      <th>新加坡</th>\n",
       "      <td>0.0949</td>\n",
       "      <td>0.0876</td>\n",
       "      <td>0.0918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>韩国</th>\n",
       "      <th>韩国</th>\n",
       "      <td>0.1497</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.1551</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               产品成本     利润   销售金额\n",
       "country 销售大区                     \n",
       "中国      东北区  0.0005 0.0007 0.0006\n",
       "        东南区  0.0006 0.0006 0.0006\n",
       "        中国台湾 0.1116 0.1053 0.1089\n",
       "        中国澳门 0.1981 0.1878 0.1938\n",
       "        中国香港 0.0877 0.0827 0.0856\n",
       "        中部   0.0004 0.0004 0.0004\n",
       "        西北区  0.1553 0.1662 0.1599\n",
       "        西南区  0.2012 0.2061 0.2033\n",
       "新加坡     新加坡  0.0949 0.0876 0.0918\n",
       "韩国      韩国   0.1497 0.1625 0.1551"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "region2 = order.pivot_table(values=['产品成本','利润','销售金额'], index=['country', '销售大区'],aggfunc=(np.sum))\n",
    "pd.set_option('display.float_format', lambda x: '%.4f'% x) \n",
    "region2 = region2.div(region2.sum(axis=0), axis=1)\n",
    "region2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 从国家层面上看，韩国占总销售额的15.5%，新加坡占9.2%, 中国占比75.3%\n",
    "2. 如果以销售大区看，占比最多的是西南区占20.3%,最少的中部之占0.04%，东北区和东南区都之占0.06%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "cn_total = order[order['country']=='中国']['利润'].sum()\n",
    "sk_total = order[order['country']=='韩国']['利润'].sum()\n",
    "sg_total = order[order['country']=='新加坡']['利润'].sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,5))\n",
    "plt.pie(x=[cn_total, sk_total, sg_total], labels=['中国', '韩国', '新加坡'], autopct=\"%.2f%%\")\n",
    "plt.legend(fontsize=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "国家层面，来自中国的利润占比达到75%，韩国16.25%， 新加坡8.76%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "plt.pie(x=region2['利润'], labels=region2.index.levels[1], autopct=\"%.2f%%\")\n",
    "plt.legend(fontsize=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "西北区、澳门、新加坡、韩国是利润的主要来源地区，分别占比20.61%、18.78%、16.62%和16.25%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 商品结构分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>产品成本</th>\n",
       "      <th>利润</th>\n",
       "      <th>订单数量</th>\n",
       "      <th>销售金额</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>产品类别</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>服装</th>\n",
       "      <td>1647620.0000</td>\n",
       "      <td>683729.5800</td>\n",
       "      <td>9101</td>\n",
       "      <td>2331349.5800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>球</th>\n",
       "      <td>561988.0200</td>\n",
       "      <td>358752.2000</td>\n",
       "      <td>15205</td>\n",
       "      <td>920740.2200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>配件</th>\n",
       "      <td>20145226.0000</td>\n",
       "      <td>15346151.1000</td>\n",
       "      <td>36085</td>\n",
       "      <td>35491377.1000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              产品成本            利润   订单数量          销售金额\n",
       "产品类别                                                 \n",
       "服装    1647620.0000   683729.5800   9101  2331349.5800\n",
       "球      561988.0200   358752.2000  15205   920740.2200\n",
       "配件   20145226.0000 15346151.1000  36085 35491377.1000"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categ = order.pivot_table(values=['订单数量','产品成本','利润','销售金额'], index=['产品类别'],aggfunc=np.sum)\n",
    "categ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>产品成本</th>\n",
       "      <th>利润</th>\n",
       "      <th>订单数量</th>\n",
       "      <th>销售金额</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>产品类别</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>服装</th>\n",
       "      <td>0.0737</td>\n",
       "      <td>0.0417</td>\n",
       "      <td>0.1507</td>\n",
       "      <td>0.0602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>球</th>\n",
       "      <td>0.0251</td>\n",
       "      <td>0.0219</td>\n",
       "      <td>0.2518</td>\n",
       "      <td>0.0238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>配件</th>\n",
       "      <td>0.9012</td>\n",
       "      <td>0.9364</td>\n",
       "      <td>0.5975</td>\n",
       "      <td>0.9161</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       产品成本     利润   订单数量   销售金额\n",
       "产品类别                            \n",
       "服装   0.0737 0.0417 0.1507 0.0602\n",
       "球    0.0251 0.0219 0.2518 0.0238\n",
       "配件   0.9012 0.9364 0.5975 0.9161"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categ.div(categ.sum(axis=0), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,10))\n",
    "for i in range(1,5):\n",
    "    fig.add_subplot(2,2,i)\n",
    "    plt.pie(categ[categ.columns[i-1]], labels=categ.index, autopct='%.2f%%')\n",
    "    plt.title(categ.columns[i-1])\n",
    "    plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**93.6%的利润来自配件**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>产品成本</th>\n",
       "      <th>利润</th>\n",
       "      <th>订单数量</th>\n",
       "      <th>销售金额</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>产品名称</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>三角网架</th>\n",
       "      <td>318150.0000</td>\n",
       "      <td>103929.0000</td>\n",
       "      <td>2121</td>\n",
       "      <td>422079.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>击打手套</th>\n",
       "      <td>28600.0000</td>\n",
       "      <td>28457.0000</td>\n",
       "      <td>1430</td>\n",
       "      <td>57057.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>垒垫</th>\n",
       "      <td>89892.0000</td>\n",
       "      <td>9080.0000</td>\n",
       "      <td>908</td>\n",
       "      <td>98972.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>垒球</th>\n",
       "      <td>94284.9300</td>\n",
       "      <td>29302.0000</td>\n",
       "      <td>2167</td>\n",
       "      <td>123586.9300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>头盔</th>\n",
       "      <td>1030240.0000</td>\n",
       "      <td>385696.1000</td>\n",
       "      <td>6439</td>\n",
       "      <td>1415936.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>帽子</th>\n",
       "      <td>21900.0000</td>\n",
       "      <td>19710.0000</td>\n",
       "      <td>2190</td>\n",
       "      <td>41610.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>打击T座</th>\n",
       "      <td>24900.0000</td>\n",
       "      <td>22161.0000</td>\n",
       "      <td>249</td>\n",
       "      <td>47061.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>捕手护具</th>\n",
       "      <td>618200.0000</td>\n",
       "      <td>168594.3800</td>\n",
       "      <td>562</td>\n",
       "      <td>786794.3800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>棒球手套</th>\n",
       "      <td>14828192.0000</td>\n",
       "      <td>11912998.0000</td>\n",
       "      <td>17327</td>\n",
       "      <td>26741190.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>棒球服</th>\n",
       "      <td>926800.0000</td>\n",
       "      <td>420000.0000</td>\n",
       "      <td>3332</td>\n",
       "      <td>1346800.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>球棒与球棒袋</th>\n",
       "      <td>3687135.0000</td>\n",
       "      <td>2835065.0000</td>\n",
       "      <td>7980</td>\n",
       "      <td>6522200.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>球网</th>\n",
       "      <td>72160.0000</td>\n",
       "      <td>25912.0000</td>\n",
       "      <td>328</td>\n",
       "      <td>98072.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>皮带</th>\n",
       "      <td>40760.0000</td>\n",
       "      <td>35665.0000</td>\n",
       "      <td>1019</td>\n",
       "      <td>76425.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>硬式棒球</th>\n",
       "      <td>341758.0000</td>\n",
       "      <td>205434.0000</td>\n",
       "      <td>8068</td>\n",
       "      <td>547192.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>袜子</th>\n",
       "      <td>11360.0000</td>\n",
       "      <td>11303.2000</td>\n",
       "      <td>568</td>\n",
       "      <td>22663.2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>装备包</th>\n",
       "      <td>94557.0000</td>\n",
       "      <td>51310.0000</td>\n",
       "      <td>733</td>\n",
       "      <td>145867.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>软式棒球</th>\n",
       "      <td>125945.0900</td>\n",
       "      <td>124016.2000</td>\n",
       "      <td>4970</td>\n",
       "      <td>249961.2900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                产品成本            利润   订单数量          销售金额\n",
       "产品名称                                                   \n",
       "三角网架     318150.0000   103929.0000   2121   422079.0000\n",
       "击打手套      28600.0000    28457.0000   1430    57057.0000\n",
       "垒垫        89892.0000     9080.0000    908    98972.0000\n",
       "垒球        94284.9300    29302.0000   2167   123586.9300\n",
       "头盔      1030240.0000   385696.1000   6439  1415936.1000\n",
       "帽子        21900.0000    19710.0000   2190    41610.0000\n",
       "打击T座      24900.0000    22161.0000    249    47061.0000\n",
       "捕手护具     618200.0000   168594.3800    562   786794.3800\n",
       "棒球手套   14828192.0000 11912998.0000  17327 26741190.0000\n",
       "棒球服      926800.0000   420000.0000   3332  1346800.0000\n",
       "球棒与球棒袋  3687135.0000  2835065.0000   7980  6522200.0000\n",
       "球网        72160.0000    25912.0000    328    98072.0000\n",
       "皮带        40760.0000    35665.0000   1019    76425.0000\n",
       "硬式棒球     341758.0000   205434.0000   8068   547192.0000\n",
       "袜子        11360.0000    11303.2000    568    22663.2000\n",
       "装备包       94557.0000    51310.0000    733   145867.0000\n",
       "软式棒球     125945.0900   124016.2000   4970   249961.2900"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products = order.pivot_table(values=['订单数量','产品成本','利润','销售金额'], index=['产品名称'],aggfunc=np.sum)\n",
    "products"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>产品成本</th>\n",
       "      <th>利润</th>\n",
       "      <th>订单数量</th>\n",
       "      <th>销售金额</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>产品名称</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>三角网架</th>\n",
       "      <td>0.0142</td>\n",
       "      <td>0.0063</td>\n",
       "      <td>0.0351</td>\n",
       "      <td>0.0109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>击打手套</th>\n",
       "      <td>0.0013</td>\n",
       "      <td>0.0017</td>\n",
       "      <td>0.0237</td>\n",
       "      <td>0.0015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>垒垫</th>\n",
       "      <td>0.0040</td>\n",
       "      <td>0.0006</td>\n",
       "      <td>0.0150</td>\n",
       "      <td>0.0026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>垒球</th>\n",
       "      <td>0.0042</td>\n",
       "      <td>0.0018</td>\n",
       "      <td>0.0359</td>\n",
       "      <td>0.0032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>头盔</th>\n",
       "      <td>0.0461</td>\n",
       "      <td>0.0235</td>\n",
       "      <td>0.1066</td>\n",
       "      <td>0.0365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>帽子</th>\n",
       "      <td>0.0010</td>\n",
       "      <td>0.0012</td>\n",
       "      <td>0.0363</td>\n",
       "      <td>0.0011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>打击T座</th>\n",
       "      <td>0.0011</td>\n",
       "      <td>0.0014</td>\n",
       "      <td>0.0041</td>\n",
       "      <td>0.0012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>捕手护具</th>\n",
       "      <td>0.0277</td>\n",
       "      <td>0.0103</td>\n",
       "      <td>0.0093</td>\n",
       "      <td>0.0203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>棒球手套</th>\n",
       "      <td>0.6633</td>\n",
       "      <td>0.7269</td>\n",
       "      <td>0.2869</td>\n",
       "      <td>0.6902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>棒球服</th>\n",
       "      <td>0.0415</td>\n",
       "      <td>0.0256</td>\n",
       "      <td>0.0552</td>\n",
       "      <td>0.0348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>球棒与球棒袋</th>\n",
       "      <td>0.1649</td>\n",
       "      <td>0.1730</td>\n",
       "      <td>0.1321</td>\n",
       "      <td>0.1683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>球网</th>\n",
       "      <td>0.0032</td>\n",
       "      <td>0.0016</td>\n",
       "      <td>0.0054</td>\n",
       "      <td>0.0025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>皮带</th>\n",
       "      <td>0.0018</td>\n",
       "      <td>0.0022</td>\n",
       "      <td>0.0169</td>\n",
       "      <td>0.0020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>硬式棒球</th>\n",
       "      <td>0.0153</td>\n",
       "      <td>0.0125</td>\n",
       "      <td>0.1336</td>\n",
       "      <td>0.0141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>袜子</th>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>0.0094</td>\n",
       "      <td>0.0006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>装备包</th>\n",
       "      <td>0.0042</td>\n",
       "      <td>0.0031</td>\n",
       "      <td>0.0121</td>\n",
       "      <td>0.0038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>软式棒球</th>\n",
       "      <td>0.0056</td>\n",
       "      <td>0.0076</td>\n",
       "      <td>0.0823</td>\n",
       "      <td>0.0065</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         产品成本     利润   订单数量   销售金额\n",
       "产品名称                              \n",
       "三角网架   0.0142 0.0063 0.0351 0.0109\n",
       "击打手套   0.0013 0.0017 0.0237 0.0015\n",
       "垒垫     0.0040 0.0006 0.0150 0.0026\n",
       "垒球     0.0042 0.0018 0.0359 0.0032\n",
       "头盔     0.0461 0.0235 0.1066 0.0365\n",
       "帽子     0.0010 0.0012 0.0363 0.0011\n",
       "打击T座   0.0011 0.0014 0.0041 0.0012\n",
       "捕手护具   0.0277 0.0103 0.0093 0.0203\n",
       "棒球手套   0.6633 0.7269 0.2869 0.6902\n",
       "棒球服    0.0415 0.0256 0.0552 0.0348\n",
       "球棒与球棒袋 0.1649 0.1730 0.1321 0.1683\n",
       "球网     0.0032 0.0016 0.0054 0.0025\n",
       "皮带     0.0018 0.0022 0.0169 0.0020\n",
       "硬式棒球   0.0153 0.0125 0.1336 0.0141\n",
       "袜子     0.0005 0.0007 0.0094 0.0006\n",
       "装备包    0.0042 0.0031 0.0121 0.0038\n",
       "软式棒球   0.0056 0.0076 0.0823 0.0065"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products.div(products.sum(axis=0), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20,20))\n",
    "for i in range(1,5):\n",
    "    fig.add_subplot(2,2,i)\n",
    "    plt.barh(products.index,products[products.columns[i-1]])\n",
    "    plt.title(products.columns[i-1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "棒球手套以28.69%的订单贡献了72.69%的利润是最赚钱的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>产品成本</th>\n",
       "      <th>利润</th>\n",
       "      <th>订单数量</th>\n",
       "      <th>销售金额</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>产品型号名称</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Adidas EQT-12.75</th>\n",
       "      <td>369474.0000</td>\n",
       "      <td>297246.0000</td>\n",
       "      <td>926</td>\n",
       "      <td>666720.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All-Star Pro CM3100SBT-33.5</th>\n",
       "      <td>684800.0000</td>\n",
       "      <td>255944.0000</td>\n",
       "      <td>856</td>\n",
       "      <td>940744.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BA-150</th>\n",
       "      <td>7668.0000</td>\n",
       "      <td>8520.0000</td>\n",
       "      <td>852</td>\n",
       "      <td>16188.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BA-180</th>\n",
       "      <td>36443.0000</td>\n",
       "      <td>34834.0000</td>\n",
       "      <td>1903</td>\n",
       "      <td>71277.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BA-580</th>\n",
       "      <td>40310.0000</td>\n",
       "      <td>27800.0000</td>\n",
       "      <td>1390</td>\n",
       "      <td>68110.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baseball Cap</th>\n",
       "      <td>21900.0000</td>\n",
       "      <td>19710.0000</td>\n",
       "      <td>2190</td>\n",
       "      <td>41610.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baseball Socks</th>\n",
       "      <td>11360.0000</td>\n",
       "      <td>11303.2000</td>\n",
       "      <td>568</td>\n",
       "      <td>22663.2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bases</th>\n",
       "      <td>89892.0000</td>\n",
       "      <td>9080.0000</td>\n",
       "      <td>908</td>\n",
       "      <td>98972.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bat Gloves</th>\n",
       "      <td>28600.0000</td>\n",
       "      <td>28457.0000</td>\n",
       "      <td>1430</td>\n",
       "      <td>57057.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bat Pack</th>\n",
       "      <td>123047.0000</td>\n",
       "      <td>127290.0000</td>\n",
       "      <td>4243</td>\n",
       "      <td>250337.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Batting Net</th>\n",
       "      <td>72160.0000</td>\n",
       "      <td>25912.0000</td>\n",
       "      <td>328</td>\n",
       "      <td>98072.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bow Net</th>\n",
       "      <td>318150.0000</td>\n",
       "      <td>103929.0000</td>\n",
       "      <td>2121</td>\n",
       "      <td>422079.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Catcher's Gear</th>\n",
       "      <td>618200.0000</td>\n",
       "      <td>168594.3800</td>\n",
       "      <td>562</td>\n",
       "      <td>786794.3800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dudley-SB</th>\n",
       "      <td>26989.2000</td>\n",
       "      <td>10800.0000</td>\n",
       "      <td>540</td>\n",
       "      <td>37789.2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dudley-SBC</th>\n",
       "      <td>17108.2800</td>\n",
       "      <td>5952.0000</td>\n",
       "      <td>372</td>\n",
       "      <td>23060.2800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dudley-Thunder-SY</th>\n",
       "      <td>50187.4500</td>\n",
       "      <td>12550.0000</td>\n",
       "      <td>1255</td>\n",
       "      <td>62737.4500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Easton Small Batch-11.5</th>\n",
       "      <td>362040.0000</td>\n",
       "      <td>300838.0000</td>\n",
       "      <td>862</td>\n",
       "      <td>662878.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Easton-Z5</th>\n",
       "      <td>1030240.0000</td>\n",
       "      <td>385696.1000</td>\n",
       "      <td>6439</td>\n",
       "      <td>1415936.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HotDot-AHD12CY</th>\n",
       "      <td>95868.4800</td>\n",
       "      <td>99456.0000</td>\n",
       "      <td>3552</td>\n",
       "      <td>195324.4800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KANSA-10</th>\n",
       "      <td>70707.0000</td>\n",
       "      <td>28860.0000</td>\n",
       "      <td>1443</td>\n",
       "      <td>99567.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Leather Belt</th>\n",
       "      <td>40760.0000</td>\n",
       "      <td>35665.0000</td>\n",
       "      <td>1019</td>\n",
       "      <td>76425.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marucci CAT Composite</th>\n",
       "      <td>2025000.0000</td>\n",
       "      <td>2022975.0000</td>\n",
       "      <td>2025</td>\n",
       "      <td>4047975.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marucci CAT9</th>\n",
       "      <td>1539088.0000</td>\n",
       "      <td>684800.0000</td>\n",
       "      <td>1712</td>\n",
       "      <td>2223888.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mizuno Franchise Series-12.5</th>\n",
       "      <td>444613.0000</td>\n",
       "      <td>179927.0000</td>\n",
       "      <td>1487</td>\n",
       "      <td>624540.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mizuno MVP-12</th>\n",
       "      <td>418800.0000</td>\n",
       "      <td>487204.0000</td>\n",
       "      <td>1396</td>\n",
       "      <td>906004.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mizuno Prospect GXC112-21.5</th>\n",
       "      <td>207756.0000</td>\n",
       "      <td>42804.0000</td>\n",
       "      <td>1044</td>\n",
       "      <td>250560.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rawlings Big Hitter Batting Tee</th>\n",
       "      <td>24900.0000</td>\n",
       "      <td>22161.0000</td>\n",
       "      <td>249</td>\n",
       "      <td>47061.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rawlings Gold Glove-11.5</th>\n",
       "      <td>6378809.0000</td>\n",
       "      <td>3829200.0000</td>\n",
       "      <td>3191</td>\n",
       "      <td>10208009.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rawlings Heart of THE Hide-11.5</th>\n",
       "      <td>1547500.0000</td>\n",
       "      <td>3710905.0000</td>\n",
       "      <td>3095</td>\n",
       "      <td>5258405.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rawlings Long-Sleeve</th>\n",
       "      <td>607600.0000</td>\n",
       "      <td>260400.0000</td>\n",
       "      <td>1736</td>\n",
       "      <td>868000.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rawlings Pro Preferred PROSCM43C-34</th>\n",
       "      <td>1122000.0000</td>\n",
       "      <td>934065.0000</td>\n",
       "      <td>935</td>\n",
       "      <td>2056065.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rawlings Pro Preffered-13-FirstBaseMitt</th>\n",
       "      <td>1393200.0000</td>\n",
       "      <td>927639.0000</td>\n",
       "      <td>1161</td>\n",
       "      <td>2320839.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rawlings-100</th>\n",
       "      <td>64101.0000</td>\n",
       "      <td>27870.0000</td>\n",
       "      <td>929</td>\n",
       "      <td>91971.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rawlings-200</th>\n",
       "      <td>122529.0000</td>\n",
       "      <td>77550.0000</td>\n",
       "      <td>1551</td>\n",
       "      <td>200079.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rawlings-ASA-RIF</th>\n",
       "      <td>16284.5700</td>\n",
       "      <td>10860.0000</td>\n",
       "      <td>543</td>\n",
       "      <td>27144.5700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rawlings-R500</th>\n",
       "      <td>94557.0000</td>\n",
       "      <td>51310.0000</td>\n",
       "      <td>733</td>\n",
       "      <td>145867.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rawlings-USSSA</th>\n",
       "      <td>11876.0400</td>\n",
       "      <td>11880.0000</td>\n",
       "      <td>396</td>\n",
       "      <td>23756.0400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rawlings-deBEER</th>\n",
       "      <td>1916.0000</td>\n",
       "      <td>1820.2000</td>\n",
       "      <td>479</td>\n",
       "      <td>3736.2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Short-Sleeve Classic Jersey</th>\n",
       "      <td>319200.0000</td>\n",
       "      <td>159600.0000</td>\n",
       "      <td>1596</td>\n",
       "      <td>478800.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wilson-A2000-12.5</th>\n",
       "      <td>1899200.0000</td>\n",
       "      <td>947226.0000</td>\n",
       "      <td>2374</td>\n",
       "      <td>2846426.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                产品成本           利润  订单数量  \\\n",
       "产品型号名称                                                                    \n",
       "Adidas EQT-12.75                         369474.0000  297246.0000   926   \n",
       "All-Star Pro CM3100SBT-33.5              684800.0000  255944.0000   856   \n",
       "BA-150                                     7668.0000    8520.0000   852   \n",
       "BA-180                                    36443.0000   34834.0000  1903   \n",
       "BA-580                                    40310.0000   27800.0000  1390   \n",
       "Baseball Cap                              21900.0000   19710.0000  2190   \n",
       "Baseball Socks                            11360.0000   11303.2000   568   \n",
       "Bases                                     89892.0000    9080.0000   908   \n",
       "Bat Gloves                                28600.0000   28457.0000  1430   \n",
       "Bat Pack                                 123047.0000  127290.0000  4243   \n",
       "Batting Net                               72160.0000   25912.0000   328   \n",
       "Bow Net                                  318150.0000  103929.0000  2121   \n",
       "Catcher's Gear                           618200.0000  168594.3800   562   \n",
       "Dudley-SB                                 26989.2000   10800.0000   540   \n",
       "Dudley-SBC                                17108.2800    5952.0000   372   \n",
       "Dudley-Thunder-SY                         50187.4500   12550.0000  1255   \n",
       "Easton Small Batch-11.5                  362040.0000  300838.0000   862   \n",
       "Easton-Z5                               1030240.0000  385696.1000  6439   \n",
       "HotDot-AHD12CY                            95868.4800   99456.0000  3552   \n",
       "KANSA-10                                  70707.0000   28860.0000  1443   \n",
       "Leather Belt                              40760.0000   35665.0000  1019   \n",
       "Marucci CAT Composite                   2025000.0000 2022975.0000  2025   \n",
       "Marucci CAT9                            1539088.0000  684800.0000  1712   \n",
       "Mizuno Franchise Series-12.5             444613.0000  179927.0000  1487   \n",
       "Mizuno MVP-12                            418800.0000  487204.0000  1396   \n",
       "Mizuno Prospect GXC112-21.5              207756.0000   42804.0000  1044   \n",
       "Rawlings Big Hitter Batting Tee           24900.0000   22161.0000   249   \n",
       "Rawlings Gold Glove-11.5                6378809.0000 3829200.0000  3191   \n",
       "Rawlings Heart of THE Hide-11.5         1547500.0000 3710905.0000  3095   \n",
       "Rawlings Long-Sleeve                     607600.0000  260400.0000  1736   \n",
       "Rawlings Pro Preferred PROSCM43C-34     1122000.0000  934065.0000   935   \n",
       "Rawlings Pro Preffered-13-FirstBaseMitt 1393200.0000  927639.0000  1161   \n",
       "Rawlings-100                              64101.0000   27870.0000   929   \n",
       "Rawlings-200                             122529.0000   77550.0000  1551   \n",
       "Rawlings-ASA-RIF                          16284.5700   10860.0000   543   \n",
       "Rawlings-R500                             94557.0000   51310.0000   733   \n",
       "Rawlings-USSSA                            11876.0400   11880.0000   396   \n",
       "Rawlings-deBEER                            1916.0000    1820.2000   479   \n",
       "Short-Sleeve Classic Jersey              319200.0000  159600.0000  1596   \n",
       "Wilson-A2000-12.5                       1899200.0000  947226.0000  2374   \n",
       "\n",
       "                                                 销售金额  \n",
       "产品型号名称                                                 \n",
       "Adidas EQT-12.75                          666720.0000  \n",
       "All-Star Pro CM3100SBT-33.5               940744.0000  \n",
       "BA-150                                     16188.0000  \n",
       "BA-180                                     71277.0000  \n",
       "BA-580                                     68110.0000  \n",
       "Baseball Cap                               41610.0000  \n",
       "Baseball Socks                             22663.2000  \n",
       "Bases                                      98972.0000  \n",
       "Bat Gloves                                 57057.0000  \n",
       "Bat Pack                                  250337.0000  \n",
       "Batting Net                                98072.0000  \n",
       "Bow Net                                   422079.0000  \n",
       "Catcher's Gear                            786794.3800  \n",
       "Dudley-SB                                  37789.2000  \n",
       "Dudley-SBC                                 23060.2800  \n",
       "Dudley-Thunder-SY                          62737.4500  \n",
       "Easton Small Batch-11.5                   662878.0000  \n",
       "Easton-Z5                                1415936.1000  \n",
       "HotDot-AHD12CY                            195324.4800  \n",
       "KANSA-10                                   99567.0000  \n",
       "Leather Belt                               76425.0000  \n",
       "Marucci CAT Composite                    4047975.0000  \n",
       "Marucci CAT9                             2223888.0000  \n",
       "Mizuno Franchise Series-12.5              624540.0000  \n",
       "Mizuno MVP-12                             906004.0000  \n",
       "Mizuno Prospect GXC112-21.5               250560.0000  \n",
       "Rawlings Big Hitter Batting Tee            47061.0000  \n",
       "Rawlings Gold Glove-11.5                10208009.0000  \n",
       "Rawlings Heart of THE Hide-11.5          5258405.0000  \n",
       "Rawlings Long-Sleeve                      868000.0000  \n",
       "Rawlings Pro Preferred PROSCM43C-34      2056065.0000  \n",
       "Rawlings Pro Preffered-13-FirstBaseMitt  2320839.0000  \n",
       "Rawlings-100                               91971.0000  \n",
       "Rawlings-200                              200079.0000  \n",
       "Rawlings-ASA-RIF                           27144.5700  \n",
       "Rawlings-R500                             145867.0000  \n",
       "Rawlings-USSSA                             23756.0400  \n",
       "Rawlings-deBEER                             3736.2000  \n",
       "Short-Sleeve Classic Jersey               478800.0000  \n",
       "Wilson-A2000-12.5                        2846426.0000  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "product_name = order.pivot_table(values=['订单数量','产品成本','利润','销售金额'], index=['产品型号名称'],\n",
    "                                 aggfunc=np.sum)\n",
    "product_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>产品成本</th>\n",
       "      <th>利润</th>\n",
       "      <th>订单数量</th>\n",
       "      <th>销售金额</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>产品型号名称</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Adidas EQT-12.75</th>\n",
       "      <td>0.0165</td>\n",
       "      <td>0.0181</td>\n",
       "      <td>0.0153</td>\n",
       "      <td>0.0172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All-Star Pro CM3100SBT-33.5</th>\n",
       "      <td>0.0306</td>\n",
       "      <td>0.0156</td>\n",
       "      <td>0.0142</td>\n",
       "      <td>0.0243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BA-150</th>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.0141</td>\n",
       "      <td>0.0004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BA-180</th>\n",
       "      <td>0.0016</td>\n",
       "      <td>0.0021</td>\n",
       "      <td>0.0315</td>\n",
       "      <td>0.0018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BA-580</th>\n",
       "      <td>0.0018</td>\n",
       "      <td>0.0017</td>\n",
       "      <td>0.0230</td>\n",
       "      <td>0.0018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baseball Cap</th>\n",
       "      <td>0.0010</td>\n",
       "      <td>0.0012</td>\n",
       "      <td>0.0363</td>\n",
       "      <td>0.0011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baseball Socks</th>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>0.0094</td>\n",
       "      <td>0.0006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bases</th>\n",
       "      <td>0.0040</td>\n",
       "      <td>0.0006</td>\n",
       "      <td>0.0150</td>\n",
       "      <td>0.0026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bat Gloves</th>\n",
       "      <td>0.0013</td>\n",
       "      <td>0.0017</td>\n",
       "      <td>0.0237</td>\n",
       "      <td>0.0015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bat Pack</th>\n",
       "      <td>0.0055</td>\n",
       "      <td>0.0078</td>\n",
       "      <td>0.0703</td>\n",
       "      <td>0.0065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Batting Net</th>\n",
       "      <td>0.0032</td>\n",
       "      <td>0.0016</td>\n",
       "      <td>0.0054</td>\n",
       "      <td>0.0025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bow Net</th>\n",
       "      <td>0.0142</td>\n",
       "      <td>0.0063</td>\n",
       "      <td>0.0351</td>\n",
       "      <td>0.0109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Catcher's Gear</th>\n",
       "      <td>0.0277</td>\n",
       "      <td>0.0103</td>\n",
       "      <td>0.0093</td>\n",
       "      <td>0.0203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dudley-SB</th>\n",
       "      <td>0.0012</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>0.0089</td>\n",
       "      <td>0.0010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dudley-SBC</th>\n",
       "      <td>0.0008</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>0.0062</td>\n",
       "      <td>0.0006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dudley-Thunder-SY</th>\n",
       "      <td>0.0022</td>\n",
       "      <td>0.0008</td>\n",
       "      <td>0.0208</td>\n",
       "      <td>0.0016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Easton Small Batch-11.5</th>\n",
       "      <td>0.0162</td>\n",
       "      <td>0.0184</td>\n",
       "      <td>0.0143</td>\n",
       "      <td>0.0171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Easton-Z5</th>\n",
       "      <td>0.0461</td>\n",
       "      <td>0.0235</td>\n",
       "      <td>0.1066</td>\n",
       "      <td>0.0365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HotDot-AHD12CY</th>\n",
       "      <td>0.0043</td>\n",
       "      <td>0.0061</td>\n",
       "      <td>0.0588</td>\n",
       "      <td>0.0050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KANSA-10</th>\n",
       "      <td>0.0032</td>\n",
       "      <td>0.0018</td>\n",
       "      <td>0.0239</td>\n",
       "      <td>0.0026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Leather Belt</th>\n",
       "      <td>0.0018</td>\n",
       "      <td>0.0022</td>\n",
       "      <td>0.0169</td>\n",
       "      <td>0.0020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marucci CAT Composite</th>\n",
       "      <td>0.0906</td>\n",
       "      <td>0.1234</td>\n",
       "      <td>0.0335</td>\n",
       "      <td>0.1045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marucci CAT9</th>\n",
       "      <td>0.0688</td>\n",
       "      <td>0.0418</td>\n",
       "      <td>0.0283</td>\n",
       "      <td>0.0574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mizuno Franchise Series-12.5</th>\n",
       "      <td>0.0199</td>\n",
       "      <td>0.0110</td>\n",
       "      <td>0.0246</td>\n",
       "      <td>0.0161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mizuno MVP-12</th>\n",
       "      <td>0.0187</td>\n",
       "      <td>0.0297</td>\n",
       "      <td>0.0231</td>\n",
       "      <td>0.0234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mizuno Prospect GXC112-21.5</th>\n",
       "      <td>0.0093</td>\n",
       "      <td>0.0026</td>\n",
       "      <td>0.0173</td>\n",
       "      <td>0.0065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rawlings Big Hitter Batting Tee</th>\n",
       "      <td>0.0011</td>\n",
       "      <td>0.0014</td>\n",
       "      <td>0.0041</td>\n",
       "      <td>0.0012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rawlings Gold Glove-11.5</th>\n",
       "      <td>0.2853</td>\n",
       "      <td>0.2336</td>\n",
       "      <td>0.0528</td>\n",
       "      <td>0.2635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rawlings Heart of THE Hide-11.5</th>\n",
       "      <td>0.0692</td>\n",
       "      <td>0.2264</td>\n",
       "      <td>0.0512</td>\n",
       "      <td>0.1357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rawlings Long-Sleeve</th>\n",
       "      <td>0.0272</td>\n",
       "      <td>0.0159</td>\n",
       "      <td>0.0287</td>\n",
       "      <td>0.0224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rawlings Pro Preferred PROSCM43C-34</th>\n",
       "      <td>0.0502</td>\n",
       "      <td>0.0570</td>\n",
       "      <td>0.0155</td>\n",
       "      <td>0.0531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rawlings Pro Preffered-13-FirstBaseMitt</th>\n",
       "      <td>0.0623</td>\n",
       "      <td>0.0566</td>\n",
       "      <td>0.0192</td>\n",
       "      <td>0.0599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rawlings-100</th>\n",
       "      <td>0.0029</td>\n",
       "      <td>0.0017</td>\n",
       "      <td>0.0154</td>\n",
       "      <td>0.0024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rawlings-200</th>\n",
       "      <td>0.0055</td>\n",
       "      <td>0.0047</td>\n",
       "      <td>0.0257</td>\n",
       "      <td>0.0052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rawlings-ASA-RIF</th>\n",
       "      <td>0.0007</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>0.0090</td>\n",
       "      <td>0.0007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rawlings-R500</th>\n",
       "      <td>0.0042</td>\n",
       "      <td>0.0031</td>\n",
       "      <td>0.0121</td>\n",
       "      <td>0.0038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rawlings-USSSA</th>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>0.0066</td>\n",
       "      <td>0.0006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rawlings-deBEER</th>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0079</td>\n",
       "      <td>0.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Short-Sleeve Classic Jersey</th>\n",
       "      <td>0.0143</td>\n",
       "      <td>0.0097</td>\n",
       "      <td>0.0264</td>\n",
       "      <td>0.0124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wilson-A2000-12.5</th>\n",
       "      <td>0.0850</td>\n",
       "      <td>0.0578</td>\n",
       "      <td>0.0393</td>\n",
       "      <td>0.0735</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          产品成本     利润   订单数量   销售金额\n",
       "产品型号名称                                                             \n",
       "Adidas EQT-12.75                        0.0165 0.0181 0.0153 0.0172\n",
       "All-Star Pro CM3100SBT-33.5             0.0306 0.0156 0.0142 0.0243\n",
       "BA-150                                  0.0003 0.0005 0.0141 0.0004\n",
       "BA-180                                  0.0016 0.0021 0.0315 0.0018\n",
       "BA-580                                  0.0018 0.0017 0.0230 0.0018\n",
       "Baseball Cap                            0.0010 0.0012 0.0363 0.0011\n",
       "Baseball Socks                          0.0005 0.0007 0.0094 0.0006\n",
       "Bases                                   0.0040 0.0006 0.0150 0.0026\n",
       "Bat Gloves                              0.0013 0.0017 0.0237 0.0015\n",
       "Bat Pack                                0.0055 0.0078 0.0703 0.0065\n",
       "Batting Net                             0.0032 0.0016 0.0054 0.0025\n",
       "Bow Net                                 0.0142 0.0063 0.0351 0.0109\n",
       "Catcher's Gear                          0.0277 0.0103 0.0093 0.0203\n",
       "Dudley-SB                               0.0012 0.0007 0.0089 0.0010\n",
       "Dudley-SBC                              0.0008 0.0004 0.0062 0.0006\n",
       "Dudley-Thunder-SY                       0.0022 0.0008 0.0208 0.0016\n",
       "Easton Small Batch-11.5                 0.0162 0.0184 0.0143 0.0171\n",
       "Easton-Z5                               0.0461 0.0235 0.1066 0.0365\n",
       "HotDot-AHD12CY                          0.0043 0.0061 0.0588 0.0050\n",
       "KANSA-10                                0.0032 0.0018 0.0239 0.0026\n",
       "Leather Belt                            0.0018 0.0022 0.0169 0.0020\n",
       "Marucci CAT Composite                   0.0906 0.1234 0.0335 0.1045\n",
       "Marucci CAT9                            0.0688 0.0418 0.0283 0.0574\n",
       "Mizuno Franchise Series-12.5            0.0199 0.0110 0.0246 0.0161\n",
       "Mizuno MVP-12                           0.0187 0.0297 0.0231 0.0234\n",
       "Mizuno Prospect GXC112-21.5             0.0093 0.0026 0.0173 0.0065\n",
       "Rawlings Big Hitter Batting Tee         0.0011 0.0014 0.0041 0.0012\n",
       "Rawlings Gold Glove-11.5                0.2853 0.2336 0.0528 0.2635\n",
       "Rawlings Heart of THE Hide-11.5         0.0692 0.2264 0.0512 0.1357\n",
       "Rawlings Long-Sleeve                    0.0272 0.0159 0.0287 0.0224\n",
       "Rawlings Pro Preferred PROSCM43C-34     0.0502 0.0570 0.0155 0.0531\n",
       "Rawlings Pro Preffered-13-FirstBaseMitt 0.0623 0.0566 0.0192 0.0599\n",
       "Rawlings-100                            0.0029 0.0017 0.0154 0.0024\n",
       "Rawlings-200                            0.0055 0.0047 0.0257 0.0052\n",
       "Rawlings-ASA-RIF                        0.0007 0.0007 0.0090 0.0007\n",
       "Rawlings-R500                           0.0042 0.0031 0.0121 0.0038\n",
       "Rawlings-USSSA                          0.0005 0.0007 0.0066 0.0006\n",
       "Rawlings-deBEER                         0.0001 0.0001 0.0079 0.0001\n",
       "Short-Sleeve Classic Jersey             0.0143 0.0097 0.0264 0.0124\n",
       "Wilson-A2000-12.5                       0.0850 0.0578 0.0393 0.0735"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "product_name.div(product_name.sum(axis=0), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x2880 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,40))\n",
    "for i in range(1,5):\n",
    "    fig.add_subplot(4,1,i)\n",
    "    plt.barh(product_name.index,product_name[product_name.columns[i-1]])\n",
    "    plt.title(product_name.columns[i-1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "具体都某一种商品型号上棒球手套的Rawlings Gold Glove-11.5和Rawlings Heart of THE Hide-11.5都以5%的订单分别贡献了23.36%和22.64%的利润"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 关联分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 首先从order中的数据构造真正的订单数据，因为原数据的一条信息是一条订单的一个商品的数据。\n",
    "# 时间具体到天，假定同一天内同一个客户id的数据属于同一条订单"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>订单日期</th>\n",
       "      <th>客户ID</th>\n",
       "      <th>产品名称</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013/10/1</td>\n",
       "      <td>13093BA</td>\n",
       "      <td>[软式棒球]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013/10/1</td>\n",
       "      <td>16450BA</td>\n",
       "      <td>[硬式棒球]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013/10/1</td>\n",
       "      <td>19996BA</td>\n",
       "      <td>[硬式棒球]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013/10/1</td>\n",
       "      <td>30243BA</td>\n",
       "      <td>[硬式棒球]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013/10/10</td>\n",
       "      <td>16171BA</td>\n",
       "      <td>[硬式棒球]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27613</th>\n",
       "      <td>2016/7/9</td>\n",
       "      <td>29336BA</td>\n",
       "      <td>[头盔, 棒球手套, 棒球手套]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27614</th>\n",
       "      <td>2016/7/9</td>\n",
       "      <td>29932BA</td>\n",
       "      <td>[棒球手套, 棒球手套]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27615</th>\n",
       "      <td>2016/7/9</td>\n",
       "      <td>30918BA</td>\n",
       "      <td>[棒球手套, 头盔]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27616</th>\n",
       "      <td>2016/7/9</td>\n",
       "      <td>30981BA</td>\n",
       "      <td>[头盔, 球棒与球棒袋, 球棒与球棒袋]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27617</th>\n",
       "      <td>2016/7/9</td>\n",
       "      <td>31265BA</td>\n",
       "      <td>[棒球手套, 棒球手套, 棒球手套]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>27618 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             订单日期     客户ID                  产品名称\n",
       "0       2013/10/1  13093BA                [软式棒球]\n",
       "1       2013/10/1  16450BA                [硬式棒球]\n",
       "2       2013/10/1  19996BA                [硬式棒球]\n",
       "3       2013/10/1  30243BA                [硬式棒球]\n",
       "4      2013/10/10  16171BA                [硬式棒球]\n",
       "...           ...      ...                   ...\n",
       "27613    2016/7/9  29336BA      [头盔, 棒球手套, 棒球手套]\n",
       "27614    2016/7/9  29932BA          [棒球手套, 棒球手套]\n",
       "27615    2016/7/9  30918BA            [棒球手套, 头盔]\n",
       "27616    2016/7/9  30981BA  [头盔, 球棒与球棒袋, 球棒与球棒袋]\n",
       "27617    2016/7/9  31265BA    [棒球手套, 棒球手套, 棒球手套]\n",
       "\n",
       "[27618 rows x 3 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def gather(x):\n",
    "    return list(x)\n",
    "data = order.loc[:, ['订单日期','客户ID','产品名称']].groupby(by=['订单日期','客户ID']).agg(gather).reset_index()\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "27618条订单"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.loc[:, '产品名称'] = data['产品名称'].apply(lambda x: list(set(x)))  # 去重"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 提取订单的购买商品信息\n",
    "buy_products = data.values[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([list(['软式棒球']), list(['硬式棒球']), list(['硬式棒球']), ...,\n",
       "       list(['棒球手套', '头盔']), list(['球棒与球棒袋', '头盔']), list(['棒球手套'])],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "buy_products"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mlxtend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mlxtend.preprocessing import TransactionEncoder\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>三角网架</th>\n",
       "      <th>击打手套</th>\n",
       "      <th>垒垫</th>\n",
       "      <th>垒球</th>\n",
       "      <th>头盔</th>\n",
       "      <th>帽子</th>\n",
       "      <th>打击T座</th>\n",
       "      <th>捕手护具</th>\n",
       "      <th>棒球手套</th>\n",
       "      <th>棒球服</th>\n",
       "      <th>球棒与球棒袋</th>\n",
       "      <th>球网</th>\n",
       "      <th>皮带</th>\n",
       "      <th>硬式棒球</th>\n",
       "      <th>袜子</th>\n",
       "      <th>装备包</th>\n",
       "      <th>软式棒球</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27613</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27614</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27615</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27616</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27617</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>27618 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        三角网架   击打手套     垒垫     垒球     头盔     帽子   打击T座   捕手护具   棒球手套    棒球服  \\\n",
       "0      False  False  False  False  False  False  False  False  False  False   \n",
       "1      False  False  False  False  False  False  False  False  False  False   \n",
       "2      False  False  False  False  False  False  False  False  False  False   \n",
       "3      False  False  False  False  False  False  False  False  False  False   \n",
       "4      False  False  False  False  False  False  False  False  False  False   \n",
       "...      ...    ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "27613  False  False  False  False   True  False  False  False   True  False   \n",
       "27614  False  False  False  False  False  False  False  False   True  False   \n",
       "27615  False  False  False  False   True  False  False  False   True  False   \n",
       "27616  False  False  False  False   True  False  False  False  False  False   \n",
       "27617  False  False  False  False  False  False  False  False   True  False   \n",
       "\n",
       "       球棒与球棒袋     球网     皮带   硬式棒球     袜子    装备包   软式棒球  \n",
       "0       False  False  False  False  False  False   True  \n",
       "1       False  False  False   True  False  False  False  \n",
       "2       False  False  False   True  False  False  False  \n",
       "3       False  False  False   True  False  False  False  \n",
       "4       False  False  False   True  False  False  False  \n",
       "...       ...    ...    ...    ...    ...    ...    ...  \n",
       "27613   False  False  False  False  False  False  False  \n",
       "27614   False  False  False  False  False  False  False  \n",
       "27615   False  False  False  False  False  False  False  \n",
       "27616    True  False  False  False  False  False  False  \n",
       "27617   False  False  False  False  False  False  False  \n",
       "\n",
       "[27618 rows x 17 columns]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TE = TransactionEncoder()\n",
    "e_data = TE.fit_transform(buy_products)\n",
    "e_data = pd.DataFrame(e_data, columns=TE.columns_)\n",
    "e_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mlxtend.frequent_patterns import fpgrowth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>support</th>\n",
       "      <th>itemsets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.1800</td>\n",
       "      <td>(软式棒球)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.2921</td>\n",
       "      <td>(硬式棒球)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.3564</td>\n",
       "      <td>(棒球手套)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0329</td>\n",
       "      <td>(垒垫)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.1726</td>\n",
       "      <td>(球棒与球棒袋)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0793</td>\n",
       "      <td>(帽子)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0518</td>\n",
       "      <td>(击打手套)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.2331</td>\n",
       "      <td>(头盔)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0768</td>\n",
       "      <td>(三角网架)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.1206</td>\n",
       "      <td>(棒球服)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.0785</td>\n",
       "      <td>(垒球)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.0265</td>\n",
       "      <td>(装备包)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.0369</td>\n",
       "      <td>(皮带)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.0203</td>\n",
       "      <td>(捕手护具)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.0206</td>\n",
       "      <td>(袜子)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.0329</td>\n",
       "      <td>(棒球手套, 软式棒球)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.0338</td>\n",
       "      <td>(头盔, 软式棒球)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.0329</td>\n",
       "      <td>(棒球手套, 硬式棒球)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.0357</td>\n",
       "      <td>(球棒与球棒袋, 硬式棒球)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.0343</td>\n",
       "      <td>(球棒与球棒袋, 软式棒球)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.0436</td>\n",
       "      <td>(球棒与球棒袋, 头盔)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.0232</td>\n",
       "      <td>(球棒与球棒袋, 帽子)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.0223</td>\n",
       "      <td>(棒球服, 帽子)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.0202</td>\n",
       "      <td>(棒球手套, 击打手套)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.0218</td>\n",
       "      <td>(击打手套, 头盔)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.1003</td>\n",
       "      <td>(棒球手套, 头盔)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.0490</td>\n",
       "      <td>(头盔, 硬式棒球)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.0325</td>\n",
       "      <td>(软式棒球, 三角网架)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.0208</td>\n",
       "      <td>(头盔, 三角网架)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.0239</td>\n",
       "      <td>(棒球手套, 棒球服)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.0290</td>\n",
       "      <td>(棒球服, 硬式棒球)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.0251</td>\n",
       "      <td>(棒球服, 头盔)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.0236</td>\n",
       "      <td>(球棒与球棒袋, 棒球服)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.0323</td>\n",
       "      <td>(垒球, 头盔)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.0202</td>\n",
       "      <td>(球棒与球棒袋, 垒球)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    support        itemsets\n",
       "0    0.1800          (软式棒球)\n",
       "1    0.2921          (硬式棒球)\n",
       "2    0.3564          (棒球手套)\n",
       "3    0.0329            (垒垫)\n",
       "4    0.1726        (球棒与球棒袋)\n",
       "5    0.0793            (帽子)\n",
       "6    0.0518          (击打手套)\n",
       "7    0.2331            (头盔)\n",
       "8    0.0768          (三角网架)\n",
       "9    0.1206           (棒球服)\n",
       "10   0.0785            (垒球)\n",
       "11   0.0265           (装备包)\n",
       "12   0.0369            (皮带)\n",
       "13   0.0203          (捕手护具)\n",
       "14   0.0206            (袜子)\n",
       "15   0.0329    (棒球手套, 软式棒球)\n",
       "16   0.0338      (头盔, 软式棒球)\n",
       "17   0.0329    (棒球手套, 硬式棒球)\n",
       "18   0.0357  (球棒与球棒袋, 硬式棒球)\n",
       "19   0.0343  (球棒与球棒袋, 软式棒球)\n",
       "20   0.0436    (球棒与球棒袋, 头盔)\n",
       "21   0.0232    (球棒与球棒袋, 帽子)\n",
       "22   0.0223       (棒球服, 帽子)\n",
       "23   0.0202    (棒球手套, 击打手套)\n",
       "24   0.0218      (击打手套, 头盔)\n",
       "25   0.1003      (棒球手套, 头盔)\n",
       "26   0.0490      (头盔, 硬式棒球)\n",
       "27   0.0325    (软式棒球, 三角网架)\n",
       "28   0.0208      (头盔, 三角网架)\n",
       "29   0.0239     (棒球手套, 棒球服)\n",
       "30   0.0290     (棒球服, 硬式棒球)\n",
       "31   0.0251       (棒球服, 头盔)\n",
       "32   0.0236   (球棒与球棒袋, 棒球服)\n",
       "33   0.0323        (垒球, 头盔)\n",
       "34   0.0202    (球棒与球棒袋, 垒球)"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items = fpgrowth(e_data, min_support=0.02, use_colnames=True)\n",
    "items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>support</th>\n",
       "      <th>itemsets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.3564</td>\n",
       "      <td>(棒球手套)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.2921</td>\n",
       "      <td>(硬式棒球)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.2331</td>\n",
       "      <td>(头盔)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.1800</td>\n",
       "      <td>(软式棒球)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.1726</td>\n",
       "      <td>(球棒与球棒袋)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.1206</td>\n",
       "      <td>(棒球服)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.1003</td>\n",
       "      <td>(棒球手套, 头盔)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0793</td>\n",
       "      <td>(帽子)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.0785</td>\n",
       "      <td>(垒球)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0768</td>\n",
       "      <td>(三角网架)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0518</td>\n",
       "      <td>(击打手套)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.0490</td>\n",
       "      <td>(头盔, 硬式棒球)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.0436</td>\n",
       "      <td>(球棒与球棒袋, 头盔)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.0369</td>\n",
       "      <td>(皮带)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.0357</td>\n",
       "      <td>(球棒与球棒袋, 硬式棒球)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.0343</td>\n",
       "      <td>(球棒与球棒袋, 软式棒球)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.0338</td>\n",
       "      <td>(头盔, 软式棒球)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.0329</td>\n",
       "      <td>(棒球手套, 软式棒球)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.0329</td>\n",
       "      <td>(棒球手套, 硬式棒球)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0329</td>\n",
       "      <td>(垒垫)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.0325</td>\n",
       "      <td>(软式棒球, 三角网架)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.0323</td>\n",
       "      <td>(垒球, 头盔)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.0290</td>\n",
       "      <td>(棒球服, 硬式棒球)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.0265</td>\n",
       "      <td>(装备包)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.0251</td>\n",
       "      <td>(棒球服, 头盔)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.0239</td>\n",
       "      <td>(棒球手套, 棒球服)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.0236</td>\n",
       "      <td>(球棒与球棒袋, 棒球服)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.0232</td>\n",
       "      <td>(球棒与球棒袋, 帽子)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.0223</td>\n",
       "      <td>(棒球服, 帽子)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.0218</td>\n",
       "      <td>(击打手套, 头盔)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.0208</td>\n",
       "      <td>(头盔, 三角网架)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.0206</td>\n",
       "      <td>(袜子)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.0203</td>\n",
       "      <td>(捕手护具)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.0202</td>\n",
       "      <td>(球棒与球棒袋, 垒球)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.0202</td>\n",
       "      <td>(棒球手套, 击打手套)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    support        itemsets\n",
       "2    0.3564          (棒球手套)\n",
       "1    0.2921          (硬式棒球)\n",
       "7    0.2331            (头盔)\n",
       "0    0.1800          (软式棒球)\n",
       "4    0.1726        (球棒与球棒袋)\n",
       "9    0.1206           (棒球服)\n",
       "25   0.1003      (棒球手套, 头盔)\n",
       "5    0.0793            (帽子)\n",
       "10   0.0785            (垒球)\n",
       "8    0.0768          (三角网架)\n",
       "6    0.0518          (击打手套)\n",
       "26   0.0490      (头盔, 硬式棒球)\n",
       "20   0.0436    (球棒与球棒袋, 头盔)\n",
       "12   0.0369            (皮带)\n",
       "18   0.0357  (球棒与球棒袋, 硬式棒球)\n",
       "19   0.0343  (球棒与球棒袋, 软式棒球)\n",
       "16   0.0338      (头盔, 软式棒球)\n",
       "15   0.0329    (棒球手套, 软式棒球)\n",
       "17   0.0329    (棒球手套, 硬式棒球)\n",
       "3    0.0329            (垒垫)\n",
       "27   0.0325    (软式棒球, 三角网架)\n",
       "33   0.0323        (垒球, 头盔)\n",
       "30   0.0290     (棒球服, 硬式棒球)\n",
       "11   0.0265           (装备包)\n",
       "31   0.0251       (棒球服, 头盔)\n",
       "29   0.0239     (棒球手套, 棒球服)\n",
       "32   0.0236   (球棒与球棒袋, 棒球服)\n",
       "21   0.0232    (球棒与球棒袋, 帽子)\n",
       "22   0.0223       (棒球服, 帽子)\n",
       "24   0.0218      (击打手套, 头盔)\n",
       "28   0.0208      (头盔, 三角网架)\n",
       "14   0.0206            (袜子)\n",
       "13   0.0203          (捕手护具)\n",
       "34   0.0202    (球棒与球棒袋, 垒球)\n",
       "23   0.0202    (棒球手套, 击打手套)"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items.sort_values(by='support', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mlxtend.frequent_patterns import association_rules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>antecedents</th>\n",
       "      <th>consequents</th>\n",
       "      <th>antecedent support</th>\n",
       "      <th>consequent support</th>\n",
       "      <th>support</th>\n",
       "      <th>confidence</th>\n",
       "      <th>lift</th>\n",
       "      <th>leverage</th>\n",
       "      <th>conviction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(球棒与球棒袋)</td>\n",
       "      <td>(硬式棒球)</td>\n",
       "      <td>0.1726</td>\n",
       "      <td>0.2921</td>\n",
       "      <td>0.0357</td>\n",
       "      <td>0.2068</td>\n",
       "      <td>0.7080</td>\n",
       "      <td>-0.0147</td>\n",
       "      <td>0.8925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(球棒与球棒袋)</td>\n",
       "      <td>(头盔)</td>\n",
       "      <td>0.1726</td>\n",
       "      <td>0.2331</td>\n",
       "      <td>0.0436</td>\n",
       "      <td>0.2524</td>\n",
       "      <td>1.0824</td>\n",
       "      <td>0.0033</td>\n",
       "      <td>1.0257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(帽子)</td>\n",
       "      <td>(球棒与球棒袋)</td>\n",
       "      <td>0.0793</td>\n",
       "      <td>0.1726</td>\n",
       "      <td>0.0232</td>\n",
       "      <td>0.2922</td>\n",
       "      <td>1.6931</td>\n",
       "      <td>0.0095</td>\n",
       "      <td>1.1690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(帽子)</td>\n",
       "      <td>(棒球服)</td>\n",
       "      <td>0.0793</td>\n",
       "      <td>0.1206</td>\n",
       "      <td>0.0223</td>\n",
       "      <td>0.2817</td>\n",
       "      <td>2.3352</td>\n",
       "      <td>0.0128</td>\n",
       "      <td>1.2243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(击打手套)</td>\n",
       "      <td>(棒球手套)</td>\n",
       "      <td>0.0518</td>\n",
       "      <td>0.3564</td>\n",
       "      <td>0.0202</td>\n",
       "      <td>0.3895</td>\n",
       "      <td>1.0928</td>\n",
       "      <td>0.0017</td>\n",
       "      <td>1.0542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(击打手套)</td>\n",
       "      <td>(头盔)</td>\n",
       "      <td>0.0518</td>\n",
       "      <td>0.2331</td>\n",
       "      <td>0.0218</td>\n",
       "      <td>0.4210</td>\n",
       "      <td>1.8057</td>\n",
       "      <td>0.0097</td>\n",
       "      <td>1.3244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(棒球手套)</td>\n",
       "      <td>(头盔)</td>\n",
       "      <td>0.3564</td>\n",
       "      <td>0.2331</td>\n",
       "      <td>0.1003</td>\n",
       "      <td>0.2813</td>\n",
       "      <td>1.2065</td>\n",
       "      <td>0.0172</td>\n",
       "      <td>1.0670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(头盔)</td>\n",
       "      <td>(棒球手套)</td>\n",
       "      <td>0.2331</td>\n",
       "      <td>0.3564</td>\n",
       "      <td>0.1003</td>\n",
       "      <td>0.4300</td>\n",
       "      <td>1.2065</td>\n",
       "      <td>0.0172</td>\n",
       "      <td>1.1291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(头盔)</td>\n",
       "      <td>(硬式棒球)</td>\n",
       "      <td>0.2331</td>\n",
       "      <td>0.2921</td>\n",
       "      <td>0.0490</td>\n",
       "      <td>0.2101</td>\n",
       "      <td>0.7193</td>\n",
       "      <td>-0.0191</td>\n",
       "      <td>0.8962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(三角网架)</td>\n",
       "      <td>(软式棒球)</td>\n",
       "      <td>0.0768</td>\n",
       "      <td>0.1800</td>\n",
       "      <td>0.0325</td>\n",
       "      <td>0.4234</td>\n",
       "      <td>2.3527</td>\n",
       "      <td>0.0187</td>\n",
       "      <td>1.4222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>(三角网架)</td>\n",
       "      <td>(头盔)</td>\n",
       "      <td>0.0768</td>\n",
       "      <td>0.2331</td>\n",
       "      <td>0.0208</td>\n",
       "      <td>0.2711</td>\n",
       "      <td>1.1628</td>\n",
       "      <td>0.0029</td>\n",
       "      <td>1.0521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>(棒球服)</td>\n",
       "      <td>(硬式棒球)</td>\n",
       "      <td>0.1206</td>\n",
       "      <td>0.2921</td>\n",
       "      <td>0.0290</td>\n",
       "      <td>0.2407</td>\n",
       "      <td>0.8239</td>\n",
       "      <td>-0.0062</td>\n",
       "      <td>0.9323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>(棒球服)</td>\n",
       "      <td>(头盔)</td>\n",
       "      <td>0.1206</td>\n",
       "      <td>0.2331</td>\n",
       "      <td>0.0251</td>\n",
       "      <td>0.2083</td>\n",
       "      <td>0.8934</td>\n",
       "      <td>-0.0030</td>\n",
       "      <td>0.9686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>(垒球)</td>\n",
       "      <td>(头盔)</td>\n",
       "      <td>0.0785</td>\n",
       "      <td>0.2331</td>\n",
       "      <td>0.0323</td>\n",
       "      <td>0.4121</td>\n",
       "      <td>1.7675</td>\n",
       "      <td>0.0140</td>\n",
       "      <td>1.3044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>(垒球)</td>\n",
       "      <td>(球棒与球棒袋)</td>\n",
       "      <td>0.0785</td>\n",
       "      <td>0.1726</td>\n",
       "      <td>0.0202</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>1.4918</td>\n",
       "      <td>0.0067</td>\n",
       "      <td>1.1143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   antecedents consequents  antecedent support  consequent support  support  \\\n",
       "0     (球棒与球棒袋)      (硬式棒球)              0.1726              0.2921   0.0357   \n",
       "1     (球棒与球棒袋)        (头盔)              0.1726              0.2331   0.0436   \n",
       "2         (帽子)    (球棒与球棒袋)              0.0793              0.1726   0.0232   \n",
       "3         (帽子)       (棒球服)              0.0793              0.1206   0.0223   \n",
       "4       (击打手套)      (棒球手套)              0.0518              0.3564   0.0202   \n",
       "5       (击打手套)        (头盔)              0.0518              0.2331   0.0218   \n",
       "6       (棒球手套)        (头盔)              0.3564              0.2331   0.1003   \n",
       "7         (头盔)      (棒球手套)              0.2331              0.3564   0.1003   \n",
       "8         (头盔)      (硬式棒球)              0.2331              0.2921   0.0490   \n",
       "9       (三角网架)      (软式棒球)              0.0768              0.1800   0.0325   \n",
       "10      (三角网架)        (头盔)              0.0768              0.2331   0.0208   \n",
       "11       (棒球服)      (硬式棒球)              0.1206              0.2921   0.0290   \n",
       "12       (棒球服)        (头盔)              0.1206              0.2331   0.0251   \n",
       "13        (垒球)        (头盔)              0.0785              0.2331   0.0323   \n",
       "14        (垒球)    (球棒与球棒袋)              0.0785              0.1726   0.0202   \n",
       "\n",
       "    confidence   lift  leverage  conviction  \n",
       "0       0.2068 0.7080   -0.0147      0.8925  \n",
       "1       0.2524 1.0824    0.0033      1.0257  \n",
       "2       0.2922 1.6931    0.0095      1.1690  \n",
       "3       0.2817 2.3352    0.0128      1.2243  \n",
       "4       0.3895 1.0928    0.0017      1.0542  \n",
       "5       0.4210 1.8057    0.0097      1.3244  \n",
       "6       0.2813 1.2065    0.0172      1.0670  \n",
       "7       0.4300 1.2065    0.0172      1.1291  \n",
       "8       0.2101 0.7193   -0.0191      0.8962  \n",
       "9       0.4234 2.3527    0.0187      1.4222  \n",
       "10      0.2711 1.1628    0.0029      1.0521  \n",
       "11      0.2407 0.8239   -0.0062      0.9323  \n",
       "12      0.2083 0.8934   -0.0030      0.9686  \n",
       "13      0.4121 1.7675    0.0140      1.3044  \n",
       "14      0.2575 1.4918    0.0067      1.1143  "
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "association_rules(items, min_threshold=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>antecedents</th>\n",
       "      <th>consequents</th>\n",
       "      <th>antecedent support</th>\n",
       "      <th>consequent support</th>\n",
       "      <th>support</th>\n",
       "      <th>confidence</th>\n",
       "      <th>lift</th>\n",
       "      <th>leverage</th>\n",
       "      <th>conviction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(软式棒球)</td>\n",
       "      <td>(棒球手套)</td>\n",
       "      <td>0.1800</td>\n",
       "      <td>0.3564</td>\n",
       "      <td>0.0329</td>\n",
       "      <td>0.1831</td>\n",
       "      <td>0.5137</td>\n",
       "      <td>-0.0312</td>\n",
       "      <td>0.7878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(头盔)</td>\n",
       "      <td>(软式棒球)</td>\n",
       "      <td>0.2331</td>\n",
       "      <td>0.1800</td>\n",
       "      <td>0.0338</td>\n",
       "      <td>0.1451</td>\n",
       "      <td>0.8061</td>\n",
       "      <td>-0.0081</td>\n",
       "      <td>0.9592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(软式棒球)</td>\n",
       "      <td>(头盔)</td>\n",
       "      <td>0.1800</td>\n",
       "      <td>0.2331</td>\n",
       "      <td>0.0338</td>\n",
       "      <td>0.1879</td>\n",
       "      <td>0.8061</td>\n",
       "      <td>-0.0081</td>\n",
       "      <td>0.9443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(硬式棒球)</td>\n",
       "      <td>(棒球手套)</td>\n",
       "      <td>0.2921</td>\n",
       "      <td>0.3564</td>\n",
       "      <td>0.0329</td>\n",
       "      <td>0.1127</td>\n",
       "      <td>0.3161</td>\n",
       "      <td>-0.0712</td>\n",
       "      <td>0.7253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(球棒与球棒袋)</td>\n",
       "      <td>(硬式棒球)</td>\n",
       "      <td>0.1726</td>\n",
       "      <td>0.2921</td>\n",
       "      <td>0.0357</td>\n",
       "      <td>0.2068</td>\n",
       "      <td>0.7080</td>\n",
       "      <td>-0.0147</td>\n",
       "      <td>0.8925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(硬式棒球)</td>\n",
       "      <td>(球棒与球棒袋)</td>\n",
       "      <td>0.2921</td>\n",
       "      <td>0.1726</td>\n",
       "      <td>0.0357</td>\n",
       "      <td>0.1222</td>\n",
       "      <td>0.7080</td>\n",
       "      <td>-0.0147</td>\n",
       "      <td>0.9426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(球棒与球棒袋)</td>\n",
       "      <td>(软式棒球)</td>\n",
       "      <td>0.1726</td>\n",
       "      <td>0.1800</td>\n",
       "      <td>0.0343</td>\n",
       "      <td>0.1989</td>\n",
       "      <td>1.1051</td>\n",
       "      <td>0.0033</td>\n",
       "      <td>1.0236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(软式棒球)</td>\n",
       "      <td>(球棒与球棒袋)</td>\n",
       "      <td>0.1800</td>\n",
       "      <td>0.1726</td>\n",
       "      <td>0.0343</td>\n",
       "      <td>0.1907</td>\n",
       "      <td>1.1051</td>\n",
       "      <td>0.0033</td>\n",
       "      <td>1.0224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(球棒与球棒袋)</td>\n",
       "      <td>(头盔)</td>\n",
       "      <td>0.1726</td>\n",
       "      <td>0.2331</td>\n",
       "      <td>0.0436</td>\n",
       "      <td>0.2524</td>\n",
       "      <td>1.0824</td>\n",
       "      <td>0.0033</td>\n",
       "      <td>1.0257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(头盔)</td>\n",
       "      <td>(球棒与球棒袋)</td>\n",
       "      <td>0.2331</td>\n",
       "      <td>0.1726</td>\n",
       "      <td>0.0436</td>\n",
       "      <td>0.1868</td>\n",
       "      <td>1.0824</td>\n",
       "      <td>0.0033</td>\n",
       "      <td>1.0175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>(球棒与球棒袋)</td>\n",
       "      <td>(帽子)</td>\n",
       "      <td>0.1726</td>\n",
       "      <td>0.0793</td>\n",
       "      <td>0.0232</td>\n",
       "      <td>0.1343</td>\n",
       "      <td>1.6931</td>\n",
       "      <td>0.0095</td>\n",
       "      <td>1.0635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>(帽子)</td>\n",
       "      <td>(球棒与球棒袋)</td>\n",
       "      <td>0.0793</td>\n",
       "      <td>0.1726</td>\n",
       "      <td>0.0232</td>\n",
       "      <td>0.2922</td>\n",
       "      <td>1.6931</td>\n",
       "      <td>0.0095</td>\n",
       "      <td>1.1690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>(棒球服)</td>\n",
       "      <td>(帽子)</td>\n",
       "      <td>0.1206</td>\n",
       "      <td>0.0793</td>\n",
       "      <td>0.0223</td>\n",
       "      <td>0.1852</td>\n",
       "      <td>2.3352</td>\n",
       "      <td>0.0128</td>\n",
       "      <td>1.1299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>(帽子)</td>\n",
       "      <td>(棒球服)</td>\n",
       "      <td>0.0793</td>\n",
       "      <td>0.1206</td>\n",
       "      <td>0.0223</td>\n",
       "      <td>0.2817</td>\n",
       "      <td>2.3352</td>\n",
       "      <td>0.0128</td>\n",
       "      <td>1.2243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>(击打手套)</td>\n",
       "      <td>(棒球手套)</td>\n",
       "      <td>0.0518</td>\n",
       "      <td>0.3564</td>\n",
       "      <td>0.0202</td>\n",
       "      <td>0.3895</td>\n",
       "      <td>1.0928</td>\n",
       "      <td>0.0017</td>\n",
       "      <td>1.0542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>(击打手套)</td>\n",
       "      <td>(头盔)</td>\n",
       "      <td>0.0518</td>\n",
       "      <td>0.2331</td>\n",
       "      <td>0.0218</td>\n",
       "      <td>0.4210</td>\n",
       "      <td>1.8057</td>\n",
       "      <td>0.0097</td>\n",
       "      <td>1.3244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>(棒球手套)</td>\n",
       "      <td>(头盔)</td>\n",
       "      <td>0.3564</td>\n",
       "      <td>0.2331</td>\n",
       "      <td>0.1003</td>\n",
       "      <td>0.2813</td>\n",
       "      <td>1.2065</td>\n",
       "      <td>0.0172</td>\n",
       "      <td>1.0670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>(头盔)</td>\n",
       "      <td>(棒球手套)</td>\n",
       "      <td>0.2331</td>\n",
       "      <td>0.3564</td>\n",
       "      <td>0.1003</td>\n",
       "      <td>0.4300</td>\n",
       "      <td>1.2065</td>\n",
       "      <td>0.0172</td>\n",
       "      <td>1.1291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>(头盔)</td>\n",
       "      <td>(硬式棒球)</td>\n",
       "      <td>0.2331</td>\n",
       "      <td>0.2921</td>\n",
       "      <td>0.0490</td>\n",
       "      <td>0.2101</td>\n",
       "      <td>0.7193</td>\n",
       "      <td>-0.0191</td>\n",
       "      <td>0.8962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>(硬式棒球)</td>\n",
       "      <td>(头盔)</td>\n",
       "      <td>0.2921</td>\n",
       "      <td>0.2331</td>\n",
       "      <td>0.0490</td>\n",
       "      <td>0.1677</td>\n",
       "      <td>0.7193</td>\n",
       "      <td>-0.0191</td>\n",
       "      <td>0.9214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>(软式棒球)</td>\n",
       "      <td>(三角网架)</td>\n",
       "      <td>0.1800</td>\n",
       "      <td>0.0768</td>\n",
       "      <td>0.0325</td>\n",
       "      <td>0.1807</td>\n",
       "      <td>2.3527</td>\n",
       "      <td>0.0187</td>\n",
       "      <td>1.1268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>(三角网架)</td>\n",
       "      <td>(软式棒球)</td>\n",
       "      <td>0.0768</td>\n",
       "      <td>0.1800</td>\n",
       "      <td>0.0325</td>\n",
       "      <td>0.4234</td>\n",
       "      <td>2.3527</td>\n",
       "      <td>0.0187</td>\n",
       "      <td>1.4222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>(三角网架)</td>\n",
       "      <td>(头盔)</td>\n",
       "      <td>0.0768</td>\n",
       "      <td>0.2331</td>\n",
       "      <td>0.0208</td>\n",
       "      <td>0.2711</td>\n",
       "      <td>1.1628</td>\n",
       "      <td>0.0029</td>\n",
       "      <td>1.0521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>(棒球服)</td>\n",
       "      <td>(棒球手套)</td>\n",
       "      <td>0.1206</td>\n",
       "      <td>0.3564</td>\n",
       "      <td>0.0239</td>\n",
       "      <td>0.1981</td>\n",
       "      <td>0.5557</td>\n",
       "      <td>-0.0191</td>\n",
       "      <td>0.8025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>(棒球服)</td>\n",
       "      <td>(硬式棒球)</td>\n",
       "      <td>0.1206</td>\n",
       "      <td>0.2921</td>\n",
       "      <td>0.0290</td>\n",
       "      <td>0.2407</td>\n",
       "      <td>0.8239</td>\n",
       "      <td>-0.0062</td>\n",
       "      <td>0.9323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>(棒球服)</td>\n",
       "      <td>(头盔)</td>\n",
       "      <td>0.1206</td>\n",
       "      <td>0.2331</td>\n",
       "      <td>0.0251</td>\n",
       "      <td>0.2083</td>\n",
       "      <td>0.8934</td>\n",
       "      <td>-0.0030</td>\n",
       "      <td>0.9686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>(头盔)</td>\n",
       "      <td>(棒球服)</td>\n",
       "      <td>0.2331</td>\n",
       "      <td>0.1206</td>\n",
       "      <td>0.0251</td>\n",
       "      <td>0.1078</td>\n",
       "      <td>0.8934</td>\n",
       "      <td>-0.0030</td>\n",
       "      <td>0.9856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>(球棒与球棒袋)</td>\n",
       "      <td>(棒球服)</td>\n",
       "      <td>0.1726</td>\n",
       "      <td>0.1206</td>\n",
       "      <td>0.0236</td>\n",
       "      <td>0.1370</td>\n",
       "      <td>1.1354</td>\n",
       "      <td>0.0028</td>\n",
       "      <td>1.0189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>(棒球服)</td>\n",
       "      <td>(球棒与球棒袋)</td>\n",
       "      <td>0.1206</td>\n",
       "      <td>0.1726</td>\n",
       "      <td>0.0236</td>\n",
       "      <td>0.1960</td>\n",
       "      <td>1.1354</td>\n",
       "      <td>0.0028</td>\n",
       "      <td>1.0291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>(垒球)</td>\n",
       "      <td>(头盔)</td>\n",
       "      <td>0.0785</td>\n",
       "      <td>0.2331</td>\n",
       "      <td>0.0323</td>\n",
       "      <td>0.4121</td>\n",
       "      <td>1.7675</td>\n",
       "      <td>0.0140</td>\n",
       "      <td>1.3044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>(头盔)</td>\n",
       "      <td>(垒球)</td>\n",
       "      <td>0.2331</td>\n",
       "      <td>0.0785</td>\n",
       "      <td>0.0323</td>\n",
       "      <td>0.1387</td>\n",
       "      <td>1.7675</td>\n",
       "      <td>0.0140</td>\n",
       "      <td>1.0699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>(球棒与球棒袋)</td>\n",
       "      <td>(垒球)</td>\n",
       "      <td>0.1726</td>\n",
       "      <td>0.0785</td>\n",
       "      <td>0.0202</td>\n",
       "      <td>0.1171</td>\n",
       "      <td>1.4918</td>\n",
       "      <td>0.0067</td>\n",
       "      <td>1.0437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>(垒球)</td>\n",
       "      <td>(球棒与球棒袋)</td>\n",
       "      <td>0.0785</td>\n",
       "      <td>0.1726</td>\n",
       "      <td>0.0202</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>1.4918</td>\n",
       "      <td>0.0067</td>\n",
       "      <td>1.1143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   antecedents consequents  antecedent support  consequent support  support  \\\n",
       "0       (软式棒球)      (棒球手套)              0.1800              0.3564   0.0329   \n",
       "1         (头盔)      (软式棒球)              0.2331              0.1800   0.0338   \n",
       "2       (软式棒球)        (头盔)              0.1800              0.2331   0.0338   \n",
       "3       (硬式棒球)      (棒球手套)              0.2921              0.3564   0.0329   \n",
       "4     (球棒与球棒袋)      (硬式棒球)              0.1726              0.2921   0.0357   \n",
       "5       (硬式棒球)    (球棒与球棒袋)              0.2921              0.1726   0.0357   \n",
       "6     (球棒与球棒袋)      (软式棒球)              0.1726              0.1800   0.0343   \n",
       "7       (软式棒球)    (球棒与球棒袋)              0.1800              0.1726   0.0343   \n",
       "8     (球棒与球棒袋)        (头盔)              0.1726              0.2331   0.0436   \n",
       "9         (头盔)    (球棒与球棒袋)              0.2331              0.1726   0.0436   \n",
       "10    (球棒与球棒袋)        (帽子)              0.1726              0.0793   0.0232   \n",
       "11        (帽子)    (球棒与球棒袋)              0.0793              0.1726   0.0232   \n",
       "12       (棒球服)        (帽子)              0.1206              0.0793   0.0223   \n",
       "13        (帽子)       (棒球服)              0.0793              0.1206   0.0223   \n",
       "14      (击打手套)      (棒球手套)              0.0518              0.3564   0.0202   \n",
       "15      (击打手套)        (头盔)              0.0518              0.2331   0.0218   \n",
       "16      (棒球手套)        (头盔)              0.3564              0.2331   0.1003   \n",
       "17        (头盔)      (棒球手套)              0.2331              0.3564   0.1003   \n",
       "18        (头盔)      (硬式棒球)              0.2331              0.2921   0.0490   \n",
       "19      (硬式棒球)        (头盔)              0.2921              0.2331   0.0490   \n",
       "20      (软式棒球)      (三角网架)              0.1800              0.0768   0.0325   \n",
       "21      (三角网架)      (软式棒球)              0.0768              0.1800   0.0325   \n",
       "22      (三角网架)        (头盔)              0.0768              0.2331   0.0208   \n",
       "23       (棒球服)      (棒球手套)              0.1206              0.3564   0.0239   \n",
       "24       (棒球服)      (硬式棒球)              0.1206              0.2921   0.0290   \n",
       "25       (棒球服)        (头盔)              0.1206              0.2331   0.0251   \n",
       "26        (头盔)       (棒球服)              0.2331              0.1206   0.0251   \n",
       "27    (球棒与球棒袋)       (棒球服)              0.1726              0.1206   0.0236   \n",
       "28       (棒球服)    (球棒与球棒袋)              0.1206              0.1726   0.0236   \n",
       "29        (垒球)        (头盔)              0.0785              0.2331   0.0323   \n",
       "30        (头盔)        (垒球)              0.2331              0.0785   0.0323   \n",
       "31    (球棒与球棒袋)        (垒球)              0.1726              0.0785   0.0202   \n",
       "32        (垒球)    (球棒与球棒袋)              0.0785              0.1726   0.0202   \n",
       "\n",
       "    confidence   lift  leverage  conviction  \n",
       "0       0.1831 0.5137   -0.0312      0.7878  \n",
       "1       0.1451 0.8061   -0.0081      0.9592  \n",
       "2       0.1879 0.8061   -0.0081      0.9443  \n",
       "3       0.1127 0.3161   -0.0712      0.7253  \n",
       "4       0.2068 0.7080   -0.0147      0.8925  \n",
       "5       0.1222 0.7080   -0.0147      0.9426  \n",
       "6       0.1989 1.1051    0.0033      1.0236  \n",
       "7       0.1907 1.1051    0.0033      1.0224  \n",
       "8       0.2524 1.0824    0.0033      1.0257  \n",
       "9       0.1868 1.0824    0.0033      1.0175  \n",
       "10      0.1343 1.6931    0.0095      1.0635  \n",
       "11      0.2922 1.6931    0.0095      1.1690  \n",
       "12      0.1852 2.3352    0.0128      1.1299  \n",
       "13      0.2817 2.3352    0.0128      1.2243  \n",
       "14      0.3895 1.0928    0.0017      1.0542  \n",
       "15      0.4210 1.8057    0.0097      1.3244  \n",
       "16      0.2813 1.2065    0.0172      1.0670  \n",
       "17      0.4300 1.2065    0.0172      1.1291  \n",
       "18      0.2101 0.7193   -0.0191      0.8962  \n",
       "19      0.1677 0.7193   -0.0191      0.9214  \n",
       "20      0.1807 2.3527    0.0187      1.1268  \n",
       "21      0.4234 2.3527    0.0187      1.4222  \n",
       "22      0.2711 1.1628    0.0029      1.0521  \n",
       "23      0.1981 0.5557   -0.0191      0.8025  \n",
       "24      0.2407 0.8239   -0.0062      0.9323  \n",
       "25      0.2083 0.8934   -0.0030      0.9686  \n",
       "26      0.1078 0.8934   -0.0030      0.9856  \n",
       "27      0.1370 1.1354    0.0028      1.0189  \n",
       "28      0.1960 1.1354    0.0028      1.0291  \n",
       "29      0.4121 1.7675    0.0140      1.3044  \n",
       "30      0.1387 1.7675    0.0140      1.0699  \n",
       "31      0.1171 1.4918    0.0067      1.0437  \n",
       "32      0.2575 1.4918    0.0067      1.1143  "
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "association_rules(items, min_threshold=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>antecedents</th>\n",
       "      <th>consequents</th>\n",
       "      <th>antecedent support</th>\n",
       "      <th>consequent support</th>\n",
       "      <th>support</th>\n",
       "      <th>confidence</th>\n",
       "      <th>lift</th>\n",
       "      <th>leverage</th>\n",
       "      <th>conviction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(击打手套)</td>\n",
       "      <td>(棒球手套)</td>\n",
       "      <td>0.0518</td>\n",
       "      <td>0.3564</td>\n",
       "      <td>0.0202</td>\n",
       "      <td>0.3895</td>\n",
       "      <td>1.0928</td>\n",
       "      <td>0.0017</td>\n",
       "      <td>1.0542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(击打手套)</td>\n",
       "      <td>(头盔)</td>\n",
       "      <td>0.0518</td>\n",
       "      <td>0.2331</td>\n",
       "      <td>0.0218</td>\n",
       "      <td>0.4210</td>\n",
       "      <td>1.8057</td>\n",
       "      <td>0.0097</td>\n",
       "      <td>1.3244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(头盔)</td>\n",
       "      <td>(棒球手套)</td>\n",
       "      <td>0.2331</td>\n",
       "      <td>0.3564</td>\n",
       "      <td>0.1003</td>\n",
       "      <td>0.4300</td>\n",
       "      <td>1.2065</td>\n",
       "      <td>0.0172</td>\n",
       "      <td>1.1291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(三角网架)</td>\n",
       "      <td>(软式棒球)</td>\n",
       "      <td>0.0768</td>\n",
       "      <td>0.1800</td>\n",
       "      <td>0.0325</td>\n",
       "      <td>0.4234</td>\n",
       "      <td>2.3527</td>\n",
       "      <td>0.0187</td>\n",
       "      <td>1.4222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(垒球)</td>\n",
       "      <td>(头盔)</td>\n",
       "      <td>0.0785</td>\n",
       "      <td>0.2331</td>\n",
       "      <td>0.0323</td>\n",
       "      <td>0.4121</td>\n",
       "      <td>1.7675</td>\n",
       "      <td>0.0140</td>\n",
       "      <td>1.3044</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  antecedents consequents  antecedent support  consequent support  support  \\\n",
       "0      (击打手套)      (棒球手套)              0.0518              0.3564   0.0202   \n",
       "1      (击打手套)        (头盔)              0.0518              0.2331   0.0218   \n",
       "2        (头盔)      (棒球手套)              0.2331              0.3564   0.1003   \n",
       "3      (三角网架)      (软式棒球)              0.0768              0.1800   0.0325   \n",
       "4        (垒球)        (头盔)              0.0785              0.2331   0.0323   \n",
       "\n",
       "   confidence   lift  leverage  conviction  \n",
       "0      0.3895 1.0928    0.0017      1.0542  \n",
       "1      0.4210 1.8057    0.0097      1.3244  \n",
       "2      0.4300 1.2065    0.0172      1.1291  \n",
       "3      0.4234 2.3527    0.0187      1.4222  \n",
       "4      0.4121 1.7675    0.0140      1.3044  "
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rules = association_rules(items, min_threshold=0.3)\n",
    "rules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 在0.3置信度的情况下产生了5条强关联规则"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "击打手套 → 棒球手套\n",
      "购买击打手套的条件下有38.95%概率买棒球手套\n",
      "\n",
      "击打手套 → 头盔\n",
      "购买击打手套的条件下有42.10%概率买头盔\n",
      "\n",
      "头盔 → 棒球手套\n",
      "购买头盔的条件下有43.00%概率买棒球手套\n",
      "\n",
      "三角网架 → 软式棒球\n",
      "购买三角网架的条件下有42.34%概率买软式棒球\n",
      "\n",
      "垒球 → 头盔\n",
      "购买垒球的条件下有41.21%概率买头盔\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def print_rule(data):\n",
    "    for i, j in data.iterrows():\n",
    "        x = j['antecedents']\n",
    "        y = j['consequents']\n",
    "        x = ','.join([xx for xx in x])\n",
    "        y = ','.join(yy for yy in y)\n",
    "        print(x + ' → ' + y)\n",
    "        print('购买'+x+'的条件下有'+'{:.2f}%'.format(j['confidence']*100)+ '概率买'+y)\n",
    "        print()\n",
    "        \n",
    "print_rule(rules)\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 分区域关联分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>订单日期</th>\n",
       "      <th>客户ID</th>\n",
       "      <th>销售大区</th>\n",
       "      <th>产品名称</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013/10/1</td>\n",
       "      <td>13093BA</td>\n",
       "      <td>中国澳门</td>\n",
       "      <td>[软式棒球]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013/10/1</td>\n",
       "      <td>16450BA</td>\n",
       "      <td>中国台湾</td>\n",
       "      <td>[硬式棒球]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013/10/1</td>\n",
       "      <td>19996BA</td>\n",
       "      <td>新加坡</td>\n",
       "      <td>[硬式棒球]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013/10/1</td>\n",
       "      <td>30243BA</td>\n",
       "      <td>西北区</td>\n",
       "      <td>[硬式棒球]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013/10/10</td>\n",
       "      <td>16171BA</td>\n",
       "      <td>中国台湾</td>\n",
       "      <td>[硬式棒球]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27613</th>\n",
       "      <td>2016/7/9</td>\n",
       "      <td>29336BA</td>\n",
       "      <td>中国香港</td>\n",
       "      <td>[头盔, 棒球手套, 棒球手套]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27614</th>\n",
       "      <td>2016/7/9</td>\n",
       "      <td>29932BA</td>\n",
       "      <td>西北区</td>\n",
       "      <td>[棒球手套, 棒球手套]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27615</th>\n",
       "      <td>2016/7/9</td>\n",
       "      <td>30918BA</td>\n",
       "      <td>新加坡</td>\n",
       "      <td>[棒球手套, 头盔]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27616</th>\n",
       "      <td>2016/7/9</td>\n",
       "      <td>30981BA</td>\n",
       "      <td>中国澳门</td>\n",
       "      <td>[头盔, 球棒与球棒袋, 球棒与球棒袋]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27617</th>\n",
       "      <td>2016/7/9</td>\n",
       "      <td>31265BA</td>\n",
       "      <td>西南区</td>\n",
       "      <td>[棒球手套, 棒球手套, 棒球手套]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>27618 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             订单日期     客户ID  销售大区                  产品名称\n",
       "0       2013/10/1  13093BA  中国澳门                [软式棒球]\n",
       "1       2013/10/1  16450BA  中国台湾                [硬式棒球]\n",
       "2       2013/10/1  19996BA   新加坡                [硬式棒球]\n",
       "3       2013/10/1  30243BA   西北区                [硬式棒球]\n",
       "4      2013/10/10  16171BA  中国台湾                [硬式棒球]\n",
       "...           ...      ...   ...                   ...\n",
       "27613    2016/7/9  29336BA  中国香港      [头盔, 棒球手套, 棒球手套]\n",
       "27614    2016/7/9  29932BA   西北区          [棒球手套, 棒球手套]\n",
       "27615    2016/7/9  30918BA   新加坡            [棒球手套, 头盔]\n",
       "27616    2016/7/9  30981BA  中国澳门  [头盔, 球棒与球棒袋, 球棒与球棒袋]\n",
       "27617    2016/7/9  31265BA   西南区    [棒球手套, 棒球手套, 棒球手套]\n",
       "\n",
       "[27618 rows x 4 columns]"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = order.loc[:, ['订单日期','客户ID','产品名称','销售大区']].groupby(by=['订单日期','客户ID', '销售大区']).agg(gather).reset_index()\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['中国澳门', '中国台湾', '新加坡', '西北区', '西南区', '中国香港', '韩国', '东南区', '中部',\n",
       "       '东北区'], dtype=object)"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['销售大区'].unique() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------\n",
      "中国澳门销售大区的强关联规则\n",
      "球棒与球棒袋 → 硬式棒球\n",
      "购买球棒与球棒袋的条件下有36.28%概率买硬式棒球\n",
      "\n",
      "棒球服 → 硬式棒球\n",
      "购买棒球服的条件下有30.08%概率买硬式棒球\n",
      "\n",
      "头盔 → 硬式棒球\n",
      "购买头盔的条件下有32.61%概率买硬式棒球\n",
      "\n",
      "头盔 → 棒球手套\n",
      "购买头盔的条件下有35.56%概率买棒球手套\n",
      "\n",
      "垒球 → 头盔\n",
      "购买垒球的条件下有38.73%概率买头盔\n",
      "\n",
      "----------------------------------------\n",
      "\n",
      "----------------------------------------\n",
      "中国台湾销售大区的强关联规则\n",
      "球棒与球棒袋 → 头盔\n",
      "购买球棒与球棒袋的条件下有33.70%概率买头盔\n",
      "\n",
      "帽子 → 头盔\n",
      "购买帽子的条件下有46.06%概率买头盔\n",
      "\n",
      "棒球服 → 硬式棒球\n",
      "购买棒球服的条件下有32.10%概率买硬式棒球\n",
      "\n",
      "三角网架 → 软式棒球\n",
      "购买三角网架的条件下有64.58%概率买软式棒球\n",
      "\n",
      "棒球手套 → 头盔\n",
      "购买棒球手套的条件下有30.34%概率买头盔\n",
      "\n",
      "头盔 → 棒球手套\n",
      "购买头盔的条件下有41.57%概率买棒球手套\n",
      "\n",
      "垒球 → 头盔\n",
      "购买垒球的条件下有44.82%概率买头盔\n",
      "\n",
      "----------------------------------------\n",
      "\n",
      "----------------------------------------\n",
      "新加坡销售大区的强关联规则\n",
      "球棒与球棒袋 → 头盔\n",
      "购买球棒与球棒袋的条件下有33.58%概率买头盔\n",
      "\n",
      "球棒与球棒袋 → 软式棒球\n",
      "购买球棒与球棒袋的条件下有30.10%概率买软式棒球\n",
      "\n",
      "棒球手套 → 头盔\n",
      "购买棒球手套的条件下有30.30%概率买头盔\n",
      "\n",
      "头盔 → 棒球手套\n",
      "购买头盔的条件下有45.56%概率买棒球手套\n",
      "\n",
      "帽子 → 棒球手套\n",
      "购买帽子的条件下有36.29%概率买棒球手套\n",
      "\n",
      "帽子 → 头盔\n",
      "购买帽子的条件下有44.73%概率买头盔\n",
      "\n",
      "垒球 → 头盔\n",
      "购买垒球的条件下有47.60%概率买头盔\n",
      "\n",
      "----------------------------------------\n",
      "\n",
      "----------------------------------------\n",
      "西北区销售大区的强关联规则\n",
      "三角网架 → 软式棒球\n",
      "购买三角网架的条件下有41.39%概率买软式棒球\n",
      "\n",
      "头盔 → 棒球手套\n",
      "购买头盔的条件下有48.42%概率买棒球手套\n",
      "\n",
      "----------------------------------------\n",
      "\n",
      "----------------------------------------\n",
      "西南区销售大区的强关联规则\n",
      "头盔 → 棒球手套\n",
      "购买头盔的条件下有40.37%概率买棒球手套\n",
      "\n",
      "垒球 → 头盔\n",
      "购买垒球的条件下有41.90%概率买头盔\n",
      "\n",
      "三角网架 → 软式棒球\n",
      "购买三角网架的条件下有47.00%概率买软式棒球\n",
      "\n",
      "击打手套 → 头盔\n",
      "购买击打手套的条件下有54.14%概率买头盔\n",
      "\n",
      "----------------------------------------\n",
      "\n",
      "----------------------------------------\n",
      "中国香港销售大区的强关联规则\n",
      "球棒与球棒袋 → 头盔\n",
      "购买球棒与球棒袋的条件下有34.00%概率买头盔\n",
      "\n",
      "球棒与球棒袋 → 软式棒球\n",
      "购买球棒与球棒袋的条件下有32.00%概率买软式棒球\n",
      "\n",
      "三角网架 → 软式棒球\n",
      "购买三角网架的条件下有57.76%概率买软式棒球\n",
      "\n",
      "棒球手套 → 头盔\n",
      "购买棒球手套的条件下有30.46%概率买头盔\n",
      "\n",
      "头盔 → 棒球手套\n",
      "购买头盔的条件下有39.62%概率买棒球手套\n",
      "\n",
      "垒球 → 头盔\n",
      "购买垒球的条件下有43.41%概率买头盔\n",
      "\n",
      "帽子 → 头盔\n",
      "购买帽子的条件下有49.82%概率买头盔\n",
      "\n",
      "----------------------------------------\n",
      "\n",
      "----------------------------------------\n",
      "韩国销售大区的强关联规则\n",
      "头盔 → 棒球手套\n",
      "购买头盔的条件下有54.67%概率买棒球手套\n",
      "\n",
      "击打手套 → 棒球手套\n",
      "购买击打手套的条件下有46.99%概率买棒球手套\n",
      "\n",
      "击打手套 → 头盔\n",
      "购买击打手套的条件下有46.59%概率买头盔\n",
      "\n",
      "----------------------------------------\n",
      "\n",
      "----------------------------------------\n",
      "东南区销售大区的强关联规则\n",
      "球棒与球棒袋 → 棒球手套\n",
      "购买球棒与球棒袋的条件下有33.33%概率买棒球手套\n",
      "\n",
      "击打手套 → 球棒与球棒袋\n",
      "购买击打手套的条件下有100.00%概率买球棒与球棒袋\n",
      "\n",
      "球棒与球棒袋 → 软式棒球\n",
      "购买球棒与球棒袋的条件下有33.33%概率买软式棒球\n",
      "\n",
      "软式棒球 → 球棒与球棒袋\n",
      "购买软式棒球的条件下有50.00%概率买球棒与球棒袋\n",
      "\n",
      "球棒与球棒袋,棒球手套 → 软式棒球\n",
      "购买球棒与球棒袋,棒球手套的条件下有50.00%概率买软式棒球\n",
      "\n",
      "球棒与球棒袋,软式棒球 → 棒球手套\n",
      "购买球棒与球棒袋,软式棒球的条件下有50.00%概率买棒球手套\n",
      "\n",
      "棒球手套,软式棒球 → 球棒与球棒袋\n",
      "购买棒球手套,软式棒球的条件下有100.00%概率买球棒与球棒袋\n",
      "\n",
      "捕手护具 → 软式棒球\n",
      "购买捕手护具的条件下有100.00%概率买软式棒球\n",
      "\n",
      "捕手护具 → 球棒与球棒袋\n",
      "购买捕手护具的条件下有100.00%概率买球棒与球棒袋\n",
      "\n",
      "捕手护具,球棒与球棒袋 → 软式棒球\n",
      "购买捕手护具,球棒与球棒袋的条件下有100.00%概率买软式棒球\n",
      "\n",
      "捕手护具,软式棒球 → 球棒与球棒袋\n",
      "购买捕手护具,软式棒球的条件下有100.00%概率买球棒与球棒袋\n",
      "\n",
      "球棒与球棒袋,软式棒球 → 捕手护具\n",
      "购买球棒与球棒袋,软式棒球的条件下有50.00%概率买捕手护具\n",
      "\n",
      "捕手护具 → 球棒与球棒袋,软式棒球\n",
      "购买捕手护具的条件下有100.00%概率买球棒与球棒袋,软式棒球\n",
      "\n",
      "棒球服 → 棒球手套\n",
      "购买棒球服的条件下有50.00%概率买棒球手套\n",
      "\n",
      "棒球服 → 软式棒球\n",
      "购买棒球服的条件下有50.00%概率买软式棒球\n",
      "\n",
      "软式棒球 → 棒球服\n",
      "购买软式棒球的条件下有50.00%概率买棒球服\n",
      "\n",
      "棒球手套,棒球服 → 软式棒球\n",
      "购买棒球手套,棒球服的条件下有50.00%概率买软式棒球\n",
      "\n",
      "棒球手套,软式棒球 → 棒球服\n",
      "购买棒球手套,软式棒球的条件下有100.00%概率买棒球服\n",
      "\n",
      "棒球服,软式棒球 → 棒球手套\n",
      "购买棒球服,软式棒球的条件下有50.00%概率买棒球手套\n",
      "\n",
      "球棒与球棒袋,棒球手套 → 棒球服\n",
      "购买球棒与球棒袋,棒球手套的条件下有50.00%概率买棒球服\n",
      "\n",
      "球棒与球棒袋,棒球服 → 棒球手套\n",
      "购买球棒与球棒袋,棒球服的条件下有100.00%概率买棒球手套\n",
      "\n",
      "棒球手套,棒球服 → 球棒与球棒袋\n",
      "购买棒球手套,棒球服的条件下有50.00%概率买球棒与球棒袋\n",
      "\n",
      "球棒与球棒袋,棒球服 → 软式棒球\n",
      "购买球棒与球棒袋,棒球服的条件下有100.00%概率买软式棒球\n",
      "\n",
      "球棒与球棒袋,软式棒球 → 棒球服\n",
      "购买球棒与球棒袋,软式棒球的条件下有50.00%概率买棒球服\n",
      "\n",
      "棒球服,软式棒球 → 球棒与球棒袋\n",
      "购买棒球服,软式棒球的条件下有50.00%概率买球棒与球棒袋\n",
      "\n",
      "球棒与球棒袋,棒球手套,棒球服 → 软式棒球\n",
      "购买球棒与球棒袋,棒球手套,棒球服的条件下有100.00%概率买软式棒球\n",
      "\n",
      "球棒与球棒袋,棒球手套,软式棒球 → 棒球服\n",
      "购买球棒与球棒袋,棒球手套,软式棒球的条件下有100.00%概率买棒球服\n",
      "\n",
      "球棒与球棒袋,棒球服,软式棒球 → 棒球手套\n",
      "购买球棒与球棒袋,棒球服,软式棒球的条件下有100.00%概率买棒球手套\n",
      "\n",
      "棒球手套,棒球服,软式棒球 → 球棒与球棒袋\n",
      "购买棒球手套,棒球服,软式棒球的条件下有100.00%概率买球棒与球棒袋\n",
      "\n",
      "球棒与球棒袋,棒球手套 → 棒球服,软式棒球\n",
      "购买球棒与球棒袋,棒球手套的条件下有50.00%概率买棒球服,软式棒球\n",
      "\n",
      "球棒与球棒袋,棒球服 → 棒球手套,软式棒球\n",
      "购买球棒与球棒袋,棒球服的条件下有100.00%概率买棒球手套,软式棒球\n",
      "\n",
      "球棒与球棒袋,软式棒球 → 棒球手套,棒球服\n",
      "购买球棒与球棒袋,软式棒球的条件下有50.00%概率买棒球手套,棒球服\n",
      "\n",
      "棒球手套,棒球服 → 球棒与球棒袋,软式棒球\n",
      "购买棒球手套,棒球服的条件下有50.00%概率买球棒与球棒袋,软式棒球\n",
      "\n",
      "棒球手套,软式棒球 → 球棒与球棒袋,棒球服\n",
      "购买棒球手套,软式棒球的条件下有100.00%概率买球棒与球棒袋,棒球服\n",
      "\n",
      "棒球服,软式棒球 → 球棒与球棒袋,棒球手套\n",
      "购买棒球服,软式棒球的条件下有50.00%概率买球棒与球棒袋,棒球手套\n",
      "\n",
      "棒球服 → 三角网架\n",
      "购买棒球服的条件下有50.00%概率买三角网架\n",
      "\n",
      "三角网架 → 棒球服\n",
      "购买三角网架的条件下有66.67%概率买棒球服\n",
      "\n",
      "软式棒球 → 三角网架\n",
      "购买软式棒球的条件下有50.00%概率买三角网架\n",
      "\n",
      "三角网架 → 软式棒球\n",
      "购买三角网架的条件下有66.67%概率买软式棒球\n",
      "\n",
      "三角网架 → 棒球手套\n",
      "购买三角网架的条件下有66.67%概率买棒球手套\n",
      "\n",
      "三角网架 → 头盔\n",
      "购买三角网架的条件下有33.33%概率买头盔\n",
      "\n",
      "三角网架 → 球棒与球棒袋\n",
      "购买三角网架的条件下有33.33%概率买球棒与球棒袋\n",
      "\n",
      "棒球手套,棒球服 → 三角网架\n",
      "购买棒球手套,棒球服的条件下有50.00%概率买三角网架\n",
      "\n",
      "棒球手套,三角网架 → 棒球服\n",
      "购买棒球手套,三角网架的条件下有50.00%概率买棒球服\n",
      "\n",
      "棒球服,三角网架 → 棒球手套\n",
      "购买棒球服,三角网架的条件下有50.00%概率买棒球手套\n",
      "\n",
      "三角网架 → 棒球手套,棒球服\n",
      "购买三角网架的条件下有33.33%概率买棒球手套,棒球服\n",
      "\n",
      "棒球服,软式棒球 → 三角网架\n",
      "购买棒球服,软式棒球的条件下有100.00%概率买三角网架\n",
      "\n",
      "棒球服,三角网架 → 软式棒球\n",
      "购买棒球服,三角网架的条件下有100.00%概率买软式棒球\n",
      "\n",
      "软式棒球,三角网架 → 棒球服\n",
      "购买软式棒球,三角网架的条件下有100.00%概率买棒球服\n",
      "\n",
      "棒球服 → 软式棒球,三角网架\n",
      "购买棒球服的条件下有50.00%概率买软式棒球,三角网架\n",
      "\n",
      "软式棒球 → 棒球服,三角网架\n",
      "购买软式棒球的条件下有50.00%概率买棒球服,三角网架\n",
      "\n",
      "三角网架 → 棒球服,软式棒球\n",
      "购买三角网架的条件下有66.67%概率买棒球服,软式棒球\n",
      "\n",
      "棒球手套,软式棒球 → 三角网架\n",
      "购买棒球手套,软式棒球的条件下有100.00%概率买三角网架\n",
      "\n",
      "棒球手套,三角网架 → 软式棒球\n",
      "购买棒球手套,三角网架的条件下有50.00%概率买软式棒球\n",
      "\n",
      "软式棒球,三角网架 → 棒球手套\n",
      "购买软式棒球,三角网架的条件下有50.00%概率买棒球手套\n",
      "\n",
      "三角网架 → 棒球手套,软式棒球\n",
      "购买三角网架的条件下有33.33%概率买棒球手套,软式棒球\n",
      "\n",
      "棒球手套,棒球服,软式棒球 → 三角网架\n",
      "购买棒球手套,棒球服,软式棒球的条件下有100.00%概率买三角网架\n",
      "\n",
      "棒球手套,棒球服,三角网架 → 软式棒球\n",
      "购买棒球手套,棒球服,三角网架的条件下有100.00%概率买软式棒球\n",
      "\n",
      "棒球手套,软式棒球,三角网架 → 棒球服\n",
      "购买棒球手套,软式棒球,三角网架的条件下有100.00%概率买棒球服\n",
      "\n",
      "棒球服,软式棒球,三角网架 → 棒球手套\n",
      "购买棒球服,软式棒球,三角网架的条件下有50.00%概率买棒球手套\n",
      "\n",
      "棒球手套,棒球服 → 软式棒球,三角网架\n",
      "购买棒球手套,棒球服的条件下有50.00%概率买软式棒球,三角网架\n",
      "\n",
      "棒球手套,软式棒球 → 棒球服,三角网架\n",
      "购买棒球手套,软式棒球的条件下有100.00%概率买棒球服,三角网架\n",
      "\n",
      "棒球手套,三角网架 → 棒球服,软式棒球\n",
      "购买棒球手套,三角网架的条件下有50.00%概率买棒球服,软式棒球\n",
      "\n",
      "棒球服,软式棒球 → 棒球手套,三角网架\n",
      "购买棒球服,软式棒球的条件下有50.00%概率买棒球手套,三角网架\n",
      "\n",
      "棒球服,三角网架 → 棒球手套,软式棒球\n",
      "购买棒球服,三角网架的条件下有50.00%概率买棒球手套,软式棒球\n",
      "\n",
      "软式棒球,三角网架 → 棒球手套,棒球服\n",
      "购买软式棒球,三角网架的条件下有50.00%概率买棒球手套,棒球服\n",
      "\n",
      "三角网架 → 棒球手套,棒球服,软式棒球\n",
      "购买三角网架的条件下有33.33%概率买棒球手套,棒球服,软式棒球\n",
      "\n",
      "棒球手套,头盔 → 三角网架\n",
      "购买棒球手套,头盔的条件下有50.00%概率买三角网架\n",
      "\n",
      "棒球手套,三角网架 → 头盔\n",
      "购买棒球手套,三角网架的条件下有50.00%概率买头盔\n",
      "\n",
      "头盔,三角网架 → 棒球手套\n",
      "购买头盔,三角网架的条件下有100.00%概率买棒球手套\n",
      "\n",
      "三角网架 → 棒球手套,头盔\n",
      "购买三角网架的条件下有33.33%概率买棒球手套,头盔\n",
      "\n",
      "球棒与球棒袋,软式棒球 → 三角网架\n",
      "购买球棒与球棒袋,软式棒球的条件下有50.00%概率买三角网架\n",
      "\n",
      "球棒与球棒袋,三角网架 → 软式棒球\n",
      "购买球棒与球棒袋,三角网架的条件下有100.00%概率买软式棒球\n",
      "\n",
      "软式棒球,三角网架 → 球棒与球棒袋\n",
      "购买软式棒球,三角网架的条件下有50.00%概率买球棒与球棒袋\n",
      "\n",
      "三角网架 → 球棒与球棒袋,软式棒球\n",
      "购买三角网架的条件下有33.33%概率买球棒与球棒袋,软式棒球\n",
      "\n",
      "球棒与球棒袋,棒球服 → 三角网架\n",
      "购买球棒与球棒袋,棒球服的条件下有100.00%概率买三角网架\n",
      "\n",
      "球棒与球棒袋,三角网架 → 棒球服\n",
      "购买球棒与球棒袋,三角网架的条件下有100.00%概率买棒球服\n",
      "\n",
      "棒球服,三角网架 → 球棒与球棒袋\n",
      "购买棒球服,三角网架的条件下有50.00%概率买球棒与球棒袋\n",
      "\n",
      "三角网架 → 球棒与球棒袋,棒球服\n",
      "购买三角网架的条件下有33.33%概率买球棒与球棒袋,棒球服\n",
      "\n",
      "球棒与球棒袋,棒球手套 → 三角网架\n",
      "购买球棒与球棒袋,棒球手套的条件下有50.00%概率买三角网架\n",
      "\n",
      "球棒与球棒袋,三角网架 → 棒球手套\n",
      "购买球棒与球棒袋,三角网架的条件下有100.00%概率买棒球手套\n",
      "\n",
      "棒球手套,三角网架 → 球棒与球棒袋\n",
      "购买棒球手套,三角网架的条件下有50.00%概率买球棒与球棒袋\n",
      "\n",
      "三角网架 → 球棒与球棒袋,棒球手套\n",
      "购买三角网架的条件下有33.33%概率买球棒与球棒袋,棒球手套\n",
      "\n",
      "球棒与球棒袋,棒球服,软式棒球 → 三角网架\n",
      "购买球棒与球棒袋,棒球服,软式棒球的条件下有100.00%概率买三角网架\n",
      "\n",
      "球棒与球棒袋,棒球服,三角网架 → 软式棒球\n",
      "购买球棒与球棒袋,棒球服,三角网架的条件下有100.00%概率买软式棒球\n",
      "\n",
      "球棒与球棒袋,软式棒球,三角网架 → 棒球服\n",
      "购买球棒与球棒袋,软式棒球,三角网架的条件下有100.00%概率买棒球服\n",
      "\n",
      "棒球服,软式棒球,三角网架 → 球棒与球棒袋\n",
      "购买棒球服,软式棒球,三角网架的条件下有50.00%概率买球棒与球棒袋\n",
      "\n",
      "球棒与球棒袋,棒球服 → 软式棒球,三角网架\n",
      "购买球棒与球棒袋,棒球服的条件下有100.00%概率买软式棒球,三角网架\n",
      "\n",
      "球棒与球棒袋,软式棒球 → 棒球服,三角网架\n",
      "购买球棒与球棒袋,软式棒球的条件下有50.00%概率买棒球服,三角网架\n",
      "\n",
      "球棒与球棒袋,三角网架 → 棒球服,软式棒球\n",
      "购买球棒与球棒袋,三角网架的条件下有100.00%概率买棒球服,软式棒球\n",
      "\n",
      "棒球服,软式棒球 → 球棒与球棒袋,三角网架\n",
      "购买棒球服,软式棒球的条件下有50.00%概率买球棒与球棒袋,三角网架\n",
      "\n",
      "棒球服,三角网架 → 球棒与球棒袋,软式棒球\n",
      "购买棒球服,三角网架的条件下有50.00%概率买球棒与球棒袋,软式棒球\n",
      "\n",
      "软式棒球,三角网架 → 球棒与球棒袋,棒球服\n",
      "购买软式棒球,三角网架的条件下有50.00%概率买球棒与球棒袋,棒球服\n",
      "\n",
      "三角网架 → 球棒与球棒袋,棒球服,软式棒球\n",
      "购买三角网架的条件下有33.33%概率买球棒与球棒袋,棒球服,软式棒球\n",
      "\n",
      "球棒与球棒袋,棒球手套,软式棒球 → 三角网架\n",
      "购买球棒与球棒袋,棒球手套,软式棒球的条件下有100.00%概率买三角网架\n",
      "\n",
      "球棒与球棒袋,棒球手套,三角网架 → 软式棒球\n",
      "购买球棒与球棒袋,棒球手套,三角网架的条件下有100.00%概率买软式棒球\n",
      "\n",
      "球棒与球棒袋,软式棒球,三角网架 → 棒球手套\n",
      "购买球棒与球棒袋,软式棒球,三角网架的条件下有100.00%概率买棒球手套\n",
      "\n",
      "棒球手套,软式棒球,三角网架 → 球棒与球棒袋\n",
      "购买棒球手套,软式棒球,三角网架的条件下有100.00%概率买球棒与球棒袋\n",
      "\n",
      "球棒与球棒袋,棒球手套 → 软式棒球,三角网架\n",
      "购买球棒与球棒袋,棒球手套的条件下有50.00%概率买软式棒球,三角网架\n",
      "\n",
      "球棒与球棒袋,软式棒球 → 棒球手套,三角网架\n",
      "购买球棒与球棒袋,软式棒球的条件下有50.00%概率买棒球手套,三角网架\n",
      "\n",
      "球棒与球棒袋,三角网架 → 棒球手套,软式棒球\n",
      "购买球棒与球棒袋,三角网架的条件下有100.00%概率买棒球手套,软式棒球\n",
      "\n",
      "棒球手套,软式棒球 → 球棒与球棒袋,三角网架\n",
      "购买棒球手套,软式棒球的条件下有100.00%概率买球棒与球棒袋,三角网架\n",
      "\n",
      "棒球手套,三角网架 → 球棒与球棒袋,软式棒球\n",
      "购买棒球手套,三角网架的条件下有50.00%概率买球棒与球棒袋,软式棒球\n",
      "\n",
      "软式棒球,三角网架 → 球棒与球棒袋,棒球手套\n",
      "购买软式棒球,三角网架的条件下有50.00%概率买球棒与球棒袋,棒球手套\n",
      "\n",
      "三角网架 → 球棒与球棒袋,棒球手套,软式棒球\n",
      "购买三角网架的条件下有33.33%概率买球棒与球棒袋,棒球手套,软式棒球\n",
      "\n",
      "球棒与球棒袋,棒球手套,棒球服 → 三角网架\n",
      "购买球棒与球棒袋,棒球手套,棒球服的条件下有100.00%概率买三角网架\n",
      "\n",
      "球棒与球棒袋,棒球手套,三角网架 → 棒球服\n",
      "购买球棒与球棒袋,棒球手套,三角网架的条件下有100.00%概率买棒球服\n",
      "\n",
      "球棒与球棒袋,棒球服,三角网架 → 棒球手套\n",
      "购买球棒与球棒袋,棒球服,三角网架的条件下有100.00%概率买棒球手套\n",
      "\n",
      "棒球手套,棒球服,三角网架 → 球棒与球棒袋\n",
      "购买棒球手套,棒球服,三角网架的条件下有100.00%概率买球棒与球棒袋\n",
      "\n",
      "球棒与球棒袋,棒球手套 → 棒球服,三角网架\n",
      "购买球棒与球棒袋,棒球手套的条件下有50.00%概率买棒球服,三角网架\n",
      "\n",
      "球棒与球棒袋,棒球服 → 棒球手套,三角网架\n",
      "购买球棒与球棒袋,棒球服的条件下有100.00%概率买棒球手套,三角网架\n",
      "\n",
      "球棒与球棒袋,三角网架 → 棒球手套,棒球服\n",
      "购买球棒与球棒袋,三角网架的条件下有100.00%概率买棒球手套,棒球服\n",
      "\n",
      "棒球手套,棒球服 → 球棒与球棒袋,三角网架\n",
      "购买棒球手套,棒球服的条件下有50.00%概率买球棒与球棒袋,三角网架\n",
      "\n",
      "棒球手套,三角网架 → 球棒与球棒袋,棒球服\n",
      "购买棒球手套,三角网架的条件下有50.00%概率买球棒与球棒袋,棒球服\n",
      "\n",
      "棒球服,三角网架 → 球棒与球棒袋,棒球手套\n",
      "购买棒球服,三角网架的条件下有50.00%概率买球棒与球棒袋,棒球手套\n",
      "\n",
      "三角网架 → 球棒与球棒袋,棒球手套,棒球服\n",
      "购买三角网架的条件下有33.33%概率买球棒与球棒袋,棒球手套,棒球服\n",
      "\n",
      "球棒与球棒袋,棒球手套,棒球服,软式棒球 → 三角网架\n",
      "购买球棒与球棒袋,棒球手套,棒球服,软式棒球的条件下有100.00%概率买三角网架\n",
      "\n",
      "球棒与球棒袋,棒球服,软式棒球,三角网架 → 棒球手套\n",
      "购买球棒与球棒袋,棒球服,软式棒球,三角网架的条件下有100.00%概率买棒球手套\n",
      "\n",
      "球棒与球棒袋,棒球手套,棒球服,三角网架 → 软式棒球\n",
      "购买球棒与球棒袋,棒球手套,棒球服,三角网架的条件下有100.00%概率买软式棒球\n",
      "\n",
      "球棒与球棒袋,棒球手套,软式棒球,三角网架 → 棒球服\n",
      "购买球棒与球棒袋,棒球手套,软式棒球,三角网架的条件下有100.00%概率买棒球服\n",
      "\n",
      "棒球手套,棒球服,软式棒球,三角网架 → 球棒与球棒袋\n",
      "购买棒球手套,棒球服,软式棒球,三角网架的条件下有100.00%概率买球棒与球棒袋\n",
      "\n",
      "球棒与球棒袋,棒球服,软式棒球 → 棒球手套,三角网架\n",
      "购买球棒与球棒袋,棒球服,软式棒球的条件下有100.00%概率买棒球手套,三角网架\n",
      "\n",
      "球棒与球棒袋,棒球手套,棒球服 → 软式棒球,三角网架\n",
      "购买球棒与球棒袋,棒球手套,棒球服的条件下有100.00%概率买软式棒球,三角网架\n",
      "\n",
      "球棒与球棒袋,棒球服,三角网架 → 棒球手套,软式棒球\n",
      "购买球棒与球棒袋,棒球服,三角网架的条件下有100.00%概率买棒球手套,软式棒球\n",
      "\n",
      "球棒与球棒袋,棒球手套,软式棒球 → 棒球服,三角网架\n",
      "购买球棒与球棒袋,棒球手套,软式棒球的条件下有100.00%概率买棒球服,三角网架\n",
      "\n",
      "球棒与球棒袋,软式棒球,三角网架 → 棒球手套,棒球服\n",
      "购买球棒与球棒袋,软式棒球,三角网架的条件下有100.00%概率买棒球手套,棒球服\n",
      "\n",
      "球棒与球棒袋,棒球手套,三角网架 → 棒球服,软式棒球\n",
      "购买球棒与球棒袋,棒球手套,三角网架的条件下有100.00%概率买棒球服,软式棒球\n",
      "\n",
      "棒球手套,棒球服,软式棒球 → 球棒与球棒袋,三角网架\n",
      "购买棒球手套,棒球服,软式棒球的条件下有100.00%概率买球棒与球棒袋,三角网架\n",
      "\n",
      "棒球服,软式棒球,三角网架 → 球棒与球棒袋,棒球手套\n",
      "购买棒球服,软式棒球,三角网架的条件下有50.00%概率买球棒与球棒袋,棒球手套\n",
      "\n",
      "棒球手套,棒球服,三角网架 → 球棒与球棒袋,软式棒球\n",
      "购买棒球手套,棒球服,三角网架的条件下有100.00%概率买球棒与球棒袋,软式棒球\n",
      "\n",
      "棒球手套,软式棒球,三角网架 → 球棒与球棒袋,棒球服\n",
      "购买棒球手套,软式棒球,三角网架的条件下有100.00%概率买球棒与球棒袋,棒球服\n",
      "\n",
      "球棒与球棒袋,棒球服 → 棒球手套,软式棒球,三角网架\n",
      "购买球棒与球棒袋,棒球服的条件下有100.00%概率买棒球手套,软式棒球,三角网架\n",
      "\n",
      "球棒与球棒袋,软式棒球 → 棒球手套,棒球服,三角网架\n",
      "购买球棒与球棒袋,软式棒球的条件下有50.00%概率买棒球手套,棒球服,三角网架\n",
      "\n",
      "球棒与球棒袋,棒球手套 → 棒球服,软式棒球,三角网架\n",
      "购买球棒与球棒袋,棒球手套的条件下有50.00%概率买棒球服,软式棒球,三角网架\n",
      "\n",
      "球棒与球棒袋,三角网架 → 棒球手套,棒球服,软式棒球\n",
      "购买球棒与球棒袋,三角网架的条件下有100.00%概率买棒球手套,棒球服,软式棒球\n",
      "\n",
      "棒球服,软式棒球 → 球棒与球棒袋,棒球手套,三角网架\n",
      "购买棒球服,软式棒球的条件下有50.00%概率买球棒与球棒袋,棒球手套,三角网架\n",
      "\n",
      "棒球手套,棒球服 → 球棒与球棒袋,软式棒球,三角网架\n",
      "购买棒球手套,棒球服的条件下有50.00%概率买球棒与球棒袋,软式棒球,三角网架\n",
      "\n",
      "棒球服,三角网架 → 球棒与球棒袋,棒球手套,软式棒球\n",
      "购买棒球服,三角网架的条件下有50.00%概率买球棒与球棒袋,棒球手套,软式棒球\n",
      "\n",
      "棒球手套,软式棒球 → 球棒与球棒袋,棒球服,三角网架\n",
      "购买棒球手套,软式棒球的条件下有100.00%概率买球棒与球棒袋,棒球服,三角网架\n",
      "\n",
      "软式棒球,三角网架 → 球棒与球棒袋,棒球手套,棒球服\n",
      "购买软式棒球,三角网架的条件下有50.00%概率买球棒与球棒袋,棒球手套,棒球服\n",
      "\n",
      "棒球手套,三角网架 → 球棒与球棒袋,棒球服,软式棒球\n",
      "购买棒球手套,三角网架的条件下有50.00%概率买球棒与球棒袋,棒球服,软式棒球\n",
      "\n",
      "三角网架 → 球棒与球棒袋,棒球手套,棒球服,软式棒球\n",
      "购买三角网架的条件下有33.33%概率买球棒与球棒袋,棒球手套,棒球服,软式棒球\n",
      "\n",
      "头盔 → 棒球手套\n",
      "购买头盔的条件下有50.00%概率买棒球手套\n",
      "\n",
      "垒球 → 头盔\n",
      "购买垒球的条件下有100.00%概率买头盔\n",
      "\n",
      "垒球 → 棒球服\n",
      "购买垒球的条件下有100.00%概率买棒球服\n",
      "\n",
      "棒球服,垒球 → 头盔\n",
      "购买棒球服,垒球的条件下有100.00%概率买头盔\n",
      "\n",
      "棒球服,头盔 → 垒球\n",
      "购买棒球服,头盔的条件下有100.00%概率买垒球\n",
      "\n",
      "垒球,头盔 → 棒球服\n",
      "购买垒球,头盔的条件下有100.00%概率买棒球服\n",
      "\n",
      "垒球 → 棒球服,头盔\n",
      "购买垒球的条件下有100.00%概率买棒球服,头盔\n",
      "\n",
      "帽子 → 三角网架\n",
      "购买帽子的条件下有100.00%概率买三角网架\n",
      "\n",
      "三角网架 → 帽子\n",
      "购买三角网架的条件下有33.33%概率买帽子\n",
      "\n",
      "帽子 → 棒球服\n",
      "购买帽子的条件下有100.00%概率买棒球服\n",
      "\n",
      "帽子 → 软式棒球\n",
      "购买帽子的条件下有100.00%概率买软式棒球\n",
      "\n",
      "帽子 → 球棒与球棒袋\n",
      "购买帽子的条件下有100.00%概率买球棒与球棒袋\n",
      "\n",
      "帽子 → 棒球手套\n",
      "购买帽子的条件下有100.00%概率买棒球手套\n",
      "\n",
      "棒球服,帽子 → 三角网架\n",
      "购买棒球服,帽子的条件下有100.00%概率买三角网架\n",
      "\n",
      "棒球服,三角网架 → 帽子\n",
      "购买棒球服,三角网架的条件下有50.00%概率买帽子\n",
      "\n",
      "三角网架,帽子 → 棒球服\n",
      "购买三角网架,帽子的条件下有100.00%概率买棒球服\n",
      "\n",
      "帽子 → 棒球服,三角网架\n",
      "购买帽子的条件下有100.00%概率买棒球服,三角网架\n",
      "\n",
      "三角网架 → 棒球服,帽子\n",
      "购买三角网架的条件下有33.33%概率买棒球服,帽子\n",
      "\n",
      "软式棒球,帽子 → 三角网架\n",
      "购买软式棒球,帽子的条件下有100.00%概率买三角网架\n",
      "\n",
      "三角网架,帽子 → 软式棒球\n",
      "购买三角网架,帽子的条件下有100.00%概率买软式棒球\n",
      "\n",
      "软式棒球,三角网架 → 帽子\n",
      "购买软式棒球,三角网架的条件下有50.00%概率买帽子\n",
      "\n",
      "帽子 → 软式棒球,三角网架\n",
      "购买帽子的条件下有100.00%概率买软式棒球,三角网架\n",
      "\n",
      "三角网架 → 软式棒球,帽子\n",
      "购买三角网架的条件下有33.33%概率买软式棒球,帽子\n",
      "\n",
      "球棒与球棒袋,帽子 → 三角网架\n",
      "购买球棒与球棒袋,帽子的条件下有100.00%概率买三角网架\n",
      "\n",
      "球棒与球棒袋,三角网架 → 帽子\n",
      "购买球棒与球棒袋,三角网架的条件下有100.00%概率买帽子\n",
      "\n",
      "三角网架,帽子 → 球棒与球棒袋\n",
      "购买三角网架,帽子的条件下有100.00%概率买球棒与球棒袋\n",
      "\n",
      "帽子 → 球棒与球棒袋,三角网架\n",
      "购买帽子的条件下有100.00%概率买球棒与球棒袋,三角网架\n",
      "\n",
      "三角网架 → 球棒与球棒袋,帽子\n",
      "购买三角网架的条件下有33.33%概率买球棒与球棒袋,帽子\n",
      "\n",
      "棒球手套,帽子 → 三角网架\n",
      "购买棒球手套,帽子的条件下有100.00%概率买三角网架\n",
      "\n",
      "棒球手套,三角网架 → 帽子\n",
      "购买棒球手套,三角网架的条件下有50.00%概率买帽子\n",
      "\n",
      "三角网架,帽子 → 棒球手套\n",
      "购买三角网架,帽子的条件下有100.00%概率买棒球手套\n",
      "\n",
      "帽子 → 棒球手套,三角网架\n",
      "购买帽子的条件下有100.00%概率买棒球手套,三角网架\n",
      "\n",
      "三角网架 → 棒球手套,帽子\n",
      "购买三角网架的条件下有33.33%概率买棒球手套,帽子\n",
      "\n",
      "棒球服,软式棒球 → 帽子\n",
      "购买棒球服,软式棒球的条件下有50.00%概率买帽子\n",
      "\n",
      "棒球服,帽子 → 软式棒球\n",
      "购买棒球服,帽子的条件下有100.00%概率买软式棒球\n",
      "\n",
      "软式棒球,帽子 → 棒球服\n",
      "购买软式棒球,帽子的条件下有100.00%概率买棒球服\n",
      "\n",
      "帽子 → 棒球服,软式棒球\n",
      "购买帽子的条件下有100.00%概率买棒球服,软式棒球\n",
      "\n",
      "球棒与球棒袋,棒球服 → 帽子\n",
      "购买球棒与球棒袋,棒球服的条件下有100.00%概率买帽子\n",
      "\n",
      "球棒与球棒袋,帽子 → 棒球服\n",
      "购买球棒与球棒袋,帽子的条件下有100.00%概率买棒球服\n",
      "\n",
      "棒球服,帽子 → 球棒与球棒袋\n",
      "购买棒球服,帽子的条件下有100.00%概率买球棒与球棒袋\n",
      "\n",
      "帽子 → 球棒与球棒袋,棒球服\n",
      "购买帽子的条件下有100.00%概率买球棒与球棒袋,棒球服\n",
      "\n",
      "棒球手套,棒球服 → 帽子\n",
      "购买棒球手套,棒球服的条件下有50.00%概率买帽子\n",
      "\n",
      "棒球手套,帽子 → 棒球服\n",
      "购买棒球手套,帽子的条件下有100.00%概率买棒球服\n",
      "\n",
      "棒球服,帽子 → 棒球手套\n",
      "购买棒球服,帽子的条件下有100.00%概率买棒球手套\n",
      "\n",
      "帽子 → 棒球手套,棒球服\n",
      "购买帽子的条件下有100.00%概率买棒球手套,棒球服\n",
      "\n",
      "球棒与球棒袋,软式棒球 → 帽子\n",
      "购买球棒与球棒袋,软式棒球的条件下有50.00%概率买帽子\n",
      "\n",
      "球棒与球棒袋,帽子 → 软式棒球\n",
      "购买球棒与球棒袋,帽子的条件下有100.00%概率买软式棒球\n",
      "\n",
      "软式棒球,帽子 → 球棒与球棒袋\n",
      "购买软式棒球,帽子的条件下有100.00%概率买球棒与球棒袋\n",
      "\n",
      "帽子 → 球棒与球棒袋,软式棒球\n",
      "购买帽子的条件下有100.00%概率买球棒与球棒袋,软式棒球\n",
      "\n",
      "棒球手套,软式棒球 → 帽子\n",
      "购买棒球手套,软式棒球的条件下有100.00%概率买帽子\n",
      "\n",
      "棒球手套,帽子 → 软式棒球\n",
      "购买棒球手套,帽子的条件下有100.00%概率买软式棒球\n",
      "\n",
      "软式棒球,帽子 → 棒球手套\n",
      "购买软式棒球,帽子的条件下有100.00%概率买棒球手套\n",
      "\n",
      "帽子 → 棒球手套,软式棒球\n",
      "购买帽子的条件下有100.00%概率买棒球手套,软式棒球\n",
      "\n",
      "球棒与球棒袋,棒球手套 → 帽子\n",
      "购买球棒与球棒袋,棒球手套的条件下有50.00%概率买帽子\n",
      "\n",
      "球棒与球棒袋,帽子 → 棒球手套\n",
      "购买球棒与球棒袋,帽子的条件下有100.00%概率买棒球手套\n",
      "\n",
      "棒球手套,帽子 → 球棒与球棒袋\n",
      "购买棒球手套,帽子的条件下有100.00%概率买球棒与球棒袋\n",
      "\n",
      "帽子 → 球棒与球棒袋,棒球手套\n",
      "购买帽子的条件下有100.00%概率买球棒与球棒袋,棒球手套\n",
      "\n",
      "棒球服,软式棒球,帽子 → 三角网架\n",
      "购买棒球服,软式棒球,帽子的条件下有100.00%概率买三角网架\n",
      "\n",
      "三角网架,棒球服,帽子 → 软式棒球\n",
      "购买三角网架,棒球服,帽子的条件下有100.00%概率买软式棒球\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "棒球服,软式棒球,三角网架 → 帽子\n",
      "购买棒球服,软式棒球,三角网架的条件下有50.00%概率买帽子\n",
      "\n",
      "三角网架,软式棒球,帽子 → 棒球服\n",
      "购买三角网架,软式棒球,帽子的条件下有100.00%概率买棒球服\n",
      "\n",
      "棒球服,帽子 → 软式棒球,三角网架\n",
      "购买棒球服,帽子的条件下有100.00%概率买软式棒球,三角网架\n",
      "\n",
      "棒球服,软式棒球 → 三角网架,帽子\n",
      "购买棒球服,软式棒球的条件下有50.00%概率买三角网架,帽子\n",
      "\n",
      "棒球服,三角网架 → 软式棒球,帽子\n",
      "购买棒球服,三角网架的条件下有50.00%概率买软式棒球,帽子\n",
      "\n",
      "软式棒球,帽子 → 棒球服,三角网架\n",
      "购买软式棒球,帽子的条件下有100.00%概率买棒球服,三角网架\n",
      "\n",
      "三角网架,帽子 → 棒球服,软式棒球\n",
      "购买三角网架,帽子的条件下有100.00%概率买棒球服,软式棒球\n",
      "\n",
      "软式棒球,三角网架 → 棒球服,帽子\n",
      "购买软式棒球,三角网架的条件下有50.00%概率买棒球服,帽子\n",
      "\n",
      "帽子 → 棒球服,软式棒球,三角网架\n",
      "购买帽子的条件下有100.00%概率买棒球服,软式棒球,三角网架\n",
      "\n",
      "三角网架 → 棒球服,软式棒球,帽子\n",
      "购买三角网架的条件下有33.33%概率买棒球服,软式棒球,帽子\n",
      "\n",
      "球棒与球棒袋,棒球服,帽子 → 三角网架\n",
      "购买球棒与球棒袋,棒球服,帽子的条件下有100.00%概率买三角网架\n",
      "\n",
      "球棒与球棒袋,棒球服,三角网架 → 帽子\n",
      "购买球棒与球棒袋,棒球服,三角网架的条件下有100.00%概率买帽子\n",
      "\n",
      "球棒与球棒袋,三角网架,帽子 → 棒球服\n",
      "购买球棒与球棒袋,三角网架,帽子的条件下有100.00%概率买棒球服\n",
      "\n",
      "三角网架,棒球服,帽子 → 球棒与球棒袋\n",
      "购买三角网架,棒球服,帽子的条件下有100.00%概率买球棒与球棒袋\n",
      "\n",
      "球棒与球棒袋,棒球服 → 三角网架,帽子\n",
      "购买球棒与球棒袋,棒球服的条件下有100.00%概率买三角网架,帽子\n",
      "\n",
      "球棒与球棒袋,帽子 → 棒球服,三角网架\n",
      "购买球棒与球棒袋,帽子的条件下有100.00%概率买棒球服,三角网架\n",
      "\n",
      "球棒与球棒袋,三角网架 → 棒球服,帽子\n",
      "购买球棒与球棒袋,三角网架的条件下有100.00%概率买棒球服,帽子\n",
      "\n",
      "棒球服,帽子 → 球棒与球棒袋,三角网架\n",
      "购买棒球服,帽子的条件下有100.00%概率买球棒与球棒袋,三角网架\n",
      "\n",
      "棒球服,三角网架 → 球棒与球棒袋,帽子\n",
      "购买棒球服,三角网架的条件下有50.00%概率买球棒与球棒袋,帽子\n",
      "\n",
      "三角网架,帽子 → 球棒与球棒袋,棒球服\n",
      "购买三角网架,帽子的条件下有100.00%概率买球棒与球棒袋,棒球服\n",
      "\n",
      "帽子 → 球棒与球棒袋,棒球服,三角网架\n",
      "购买帽子的条件下有100.00%概率买球棒与球棒袋,棒球服,三角网架\n",
      "\n",
      "三角网架 → 球棒与球棒袋,棒球服,帽子\n",
      "购买三角网架的条件下有33.33%概率买球棒与球棒袋,棒球服,帽子\n",
      "\n",
      "棒球手套,棒球服,帽子 → 三角网架\n",
      "购买棒球手套,棒球服,帽子的条件下有100.00%概率买三角网架\n",
      "\n",
      "棒球手套,棒球服,三角网架 → 帽子\n",
      "购买棒球手套,棒球服,三角网架的条件下有100.00%概率买帽子\n",
      "\n",
      "棒球手套,三角网架,帽子 → 棒球服\n",
      "购买棒球手套,三角网架,帽子的条件下有100.00%概率买棒球服\n",
      "\n",
      "三角网架,棒球服,帽子 → 棒球手套\n",
      "购买三角网架,棒球服,帽子的条件下有100.00%概率买棒球手套\n",
      "\n",
      "棒球手套,棒球服 → 三角网架,帽子\n",
      "购买棒球手套,棒球服的条件下有50.00%概率买三角网架,帽子\n",
      "\n",
      "棒球手套,帽子 → 棒球服,三角网架\n",
      "购买棒球手套,帽子的条件下有100.00%概率买棒球服,三角网架\n",
      "\n",
      "棒球手套,三角网架 → 棒球服,帽子\n",
      "购买棒球手套,三角网架的条件下有50.00%概率买棒球服,帽子\n",
      "\n",
      "棒球服,帽子 → 棒球手套,三角网架\n",
      "购买棒球服,帽子的条件下有100.00%概率买棒球手套,三角网架\n",
      "\n",
      "棒球服,三角网架 → 棒球手套,帽子\n",
      "购买棒球服,三角网架的条件下有50.00%概率买棒球手套,帽子\n",
      "\n",
      "三角网架,帽子 → 棒球手套,棒球服\n",
      "购买三角网架,帽子的条件下有100.00%概率买棒球手套,棒球服\n",
      "\n",
      "帽子 → 棒球手套,棒球服,三角网架\n",
      "购买帽子的条件下有100.00%概率买棒球手套,棒球服,三角网架\n",
      "\n",
      "三角网架 → 棒球手套,棒球服,帽子\n",
      "购买三角网架的条件下有33.33%概率买棒球手套,棒球服,帽子\n",
      "\n",
      "球棒与球棒袋,软式棒球,帽子 → 三角网架\n",
      "购买球棒与球棒袋,软式棒球,帽子的条件下有100.00%概率买三角网架\n",
      "\n",
      "球棒与球棒袋,三角网架,帽子 → 软式棒球\n",
      "购买球棒与球棒袋,三角网架,帽子的条件下有100.00%概率买软式棒球\n",
      "\n",
      "球棒与球棒袋,软式棒球,三角网架 → 帽子\n",
      "购买球棒与球棒袋,软式棒球,三角网架的条件下有100.00%概率买帽子\n",
      "\n",
      "三角网架,软式棒球,帽子 → 球棒与球棒袋\n",
      "购买三角网架,软式棒球,帽子的条件下有100.00%概率买球棒与球棒袋\n",
      "\n",
      "球棒与球棒袋,帽子 → 软式棒球,三角网架\n",
      "购买球棒与球棒袋,帽子的条件下有100.00%概率买软式棒球,三角网架\n",
      "\n",
      "球棒与球棒袋,软式棒球 → 三角网架,帽子\n",
      "购买球棒与球棒袋,软式棒球的条件下有50.00%概率买三角网架,帽子\n",
      "\n",
      "球棒与球棒袋,三角网架 → 软式棒球,帽子\n",
      "购买球棒与球棒袋,三角网架的条件下有100.00%概率买软式棒球,帽子\n",
      "\n",
      "软式棒球,帽子 → 球棒与球棒袋,三角网架\n",
      "购买软式棒球,帽子的条件下有100.00%概率买球棒与球棒袋,三角网架\n",
      "\n",
      "三角网架,帽子 → 球棒与球棒袋,软式棒球\n",
      "购买三角网架,帽子的条件下有100.00%概率买球棒与球棒袋,软式棒球\n",
      "\n",
      "软式棒球,三角网架 → 球棒与球棒袋,帽子\n",
      "购买软式棒球,三角网架的条件下有50.00%概率买球棒与球棒袋,帽子\n",
      "\n",
      "帽子 → 球棒与球棒袋,软式棒球,三角网架\n",
      "购买帽子的条件下有100.00%概率买球棒与球棒袋,软式棒球,三角网架\n",
      "\n",
      "三角网架 → 球棒与球棒袋,软式棒球,帽子\n",
      "购买三角网架的条件下有33.33%概率买球棒与球棒袋,软式棒球,帽子\n",
      "\n",
      "棒球手套,软式棒球,帽子 → 三角网架\n",
      "购买棒球手套,软式棒球,帽子的条件下有100.00%概率买三角网架\n",
      "\n",
      "棒球手套,三角网架,帽子 → 软式棒球\n",
      "购买棒球手套,三角网架,帽子的条件下有100.00%概率买软式棒球\n",
      "\n",
      "棒球手套,软式棒球,三角网架 → 帽子\n",
      "购买棒球手套,软式棒球,三角网架的条件下有100.00%概率买帽子\n",
      "\n",
      "三角网架,软式棒球,帽子 → 棒球手套\n",
      "购买三角网架,软式棒球,帽子的条件下有100.00%概率买棒球手套\n",
      "\n",
      "棒球手套,帽子 → 软式棒球,三角网架\n",
      "购买棒球手套,帽子的条件下有100.00%概率买软式棒球,三角网架\n",
      "\n",
      "棒球手套,软式棒球 → 三角网架,帽子\n",
      "购买棒球手套,软式棒球的条件下有100.00%概率买三角网架,帽子\n",
      "\n",
      "棒球手套,三角网架 → 软式棒球,帽子\n",
      "购买棒球手套,三角网架的条件下有50.00%概率买软式棒球,帽子\n",
      "\n",
      "软式棒球,帽子 → 棒球手套,三角网架\n",
      "购买软式棒球,帽子的条件下有100.00%概率买棒球手套,三角网架\n",
      "\n",
      "三角网架,帽子 → 棒球手套,软式棒球\n",
      "购买三角网架,帽子的条件下有100.00%概率买棒球手套,软式棒球\n",
      "\n",
      "软式棒球,三角网架 → 棒球手套,帽子\n",
      "购买软式棒球,三角网架的条件下有50.00%概率买棒球手套,帽子\n",
      "\n",
      "帽子 → 棒球手套,软式棒球,三角网架\n",
      "购买帽子的条件下有100.00%概率买棒球手套,软式棒球,三角网架\n",
      "\n",
      "三角网架 → 棒球手套,软式棒球,帽子\n",
      "购买三角网架的条件下有33.33%概率买棒球手套,软式棒球,帽子\n",
      "\n",
      "球棒与球棒袋,棒球手套,帽子 → 三角网架\n",
      "购买球棒与球棒袋,棒球手套,帽子的条件下有100.00%概率买三角网架\n",
      "\n",
      "球棒与球棒袋,棒球手套,三角网架 → 帽子\n",
      "购买球棒与球棒袋,棒球手套,三角网架的条件下有100.00%概率买帽子\n",
      "\n",
      "球棒与球棒袋,三角网架,帽子 → 棒球手套\n",
      "购买球棒与球棒袋,三角网架,帽子的条件下有100.00%概率买棒球手套\n",
      "\n",
      "棒球手套,三角网架,帽子 → 球棒与球棒袋\n",
      "购买棒球手套,三角网架,帽子的条件下有100.00%概率买球棒与球棒袋\n",
      "\n",
      "球棒与球棒袋,棒球手套 → 三角网架,帽子\n",
      "购买球棒与球棒袋,棒球手套的条件下有50.00%概率买三角网架,帽子\n",
      "\n",
      "球棒与球棒袋,帽子 → 棒球手套,三角网架\n",
      "购买球棒与球棒袋,帽子的条件下有100.00%概率买棒球手套,三角网架\n",
      "\n",
      "球棒与球棒袋,三角网架 → 棒球手套,帽子\n",
      "购买球棒与球棒袋,三角网架的条件下有100.00%概率买棒球手套,帽子\n",
      "\n",
      "棒球手套,帽子 → 球棒与球棒袋,三角网架\n",
      "购买棒球手套,帽子的条件下有100.00%概率买球棒与球棒袋,三角网架\n",
      "\n",
      "棒球手套,三角网架 → 球棒与球棒袋,帽子\n",
      "购买棒球手套,三角网架的条件下有50.00%概率买球棒与球棒袋,帽子\n",
      "\n",
      "三角网架,帽子 → 球棒与球棒袋,棒球手套\n",
      "购买三角网架,帽子的条件下有100.00%概率买球棒与球棒袋,棒球手套\n",
      "\n",
      "帽子 → 球棒与球棒袋,棒球手套,三角网架\n",
      "购买帽子的条件下有100.00%概率买球棒与球棒袋,棒球手套,三角网架\n",
      "\n",
      "三角网架 → 球棒与球棒袋,棒球手套,帽子\n",
      "购买三角网架的条件下有33.33%概率买球棒与球棒袋,棒球手套,帽子\n",
      "\n",
      "球棒与球棒袋,棒球服,软式棒球 → 帽子\n",
      "购买球棒与球棒袋,棒球服,软式棒球的条件下有100.00%概率买帽子\n",
      "\n",
      "球棒与球棒袋,棒球服,帽子 → 软式棒球\n",
      "购买球棒与球棒袋,棒球服,帽子的条件下有100.00%概率买软式棒球\n",
      "\n",
      "球棒与球棒袋,软式棒球,帽子 → 棒球服\n",
      "购买球棒与球棒袋,软式棒球,帽子的条件下有100.00%概率买棒球服\n",
      "\n",
      "棒球服,软式棒球,帽子 → 球棒与球棒袋\n",
      "购买棒球服,软式棒球,帽子的条件下有100.00%概率买球棒与球棒袋\n",
      "\n",
      "球棒与球棒袋,棒球服 → 软式棒球,帽子\n",
      "购买球棒与球棒袋,棒球服的条件下有100.00%概率买软式棒球,帽子\n",
      "\n",
      "球棒与球棒袋,软式棒球 → 棒球服,帽子\n",
      "购买球棒与球棒袋,软式棒球的条件下有50.00%概率买棒球服,帽子\n",
      "\n",
      "球棒与球棒袋,帽子 → 棒球服,软式棒球\n",
      "购买球棒与球棒袋,帽子的条件下有100.00%概率买棒球服,软式棒球\n",
      "\n",
      "棒球服,软式棒球 → 球棒与球棒袋,帽子\n",
      "购买棒球服,软式棒球的条件下有50.00%概率买球棒与球棒袋,帽子\n",
      "\n",
      "棒球服,帽子 → 球棒与球棒袋,软式棒球\n",
      "购买棒球服,帽子的条件下有100.00%概率买球棒与球棒袋,软式棒球\n",
      "\n",
      "软式棒球,帽子 → 球棒与球棒袋,棒球服\n",
      "购买软式棒球,帽子的条件下有100.00%概率买球棒与球棒袋,棒球服\n",
      "\n",
      "帽子 → 球棒与球棒袋,棒球服,软式棒球\n",
      "购买帽子的条件下有100.00%概率买球棒与球棒袋,棒球服,软式棒球\n",
      "\n",
      "棒球手套,棒球服,软式棒球 → 帽子\n",
      "购买棒球手套,棒球服,软式棒球的条件下有100.00%概率买帽子\n",
      "\n",
      "棒球手套,棒球服,帽子 → 软式棒球\n",
      "购买棒球手套,棒球服,帽子的条件下有100.00%概率买软式棒球\n",
      "\n",
      "棒球手套,软式棒球,帽子 → 棒球服\n",
      "购买棒球手套,软式棒球,帽子的条件下有100.00%概率买棒球服\n",
      "\n",
      "棒球服,软式棒球,帽子 → 棒球手套\n",
      "购买棒球服,软式棒球,帽子的条件下有100.00%概率买棒球手套\n",
      "\n",
      "棒球手套,棒球服 → 软式棒球,帽子\n",
      "购买棒球手套,棒球服的条件下有50.00%概率买软式棒球,帽子\n",
      "\n",
      "棒球手套,软式棒球 → 棒球服,帽子\n",
      "购买棒球手套,软式棒球的条件下有100.00%概率买棒球服,帽子\n",
      "\n",
      "棒球手套,帽子 → 棒球服,软式棒球\n",
      "购买棒球手套,帽子的条件下有100.00%概率买棒球服,软式棒球\n",
      "\n",
      "棒球服,软式棒球 → 棒球手套,帽子\n",
      "购买棒球服,软式棒球的条件下有50.00%概率买棒球手套,帽子\n",
      "\n",
      "棒球服,帽子 → 棒球手套,软式棒球\n",
      "购买棒球服,帽子的条件下有100.00%概率买棒球手套,软式棒球\n",
      "\n",
      "软式棒球,帽子 → 棒球手套,棒球服\n",
      "购买软式棒球,帽子的条件下有100.00%概率买棒球手套,棒球服\n",
      "\n",
      "帽子 → 棒球手套,棒球服,软式棒球\n",
      "购买帽子的条件下有100.00%概率买棒球手套,棒球服,软式棒球\n",
      "\n",
      "球棒与球棒袋,棒球手套,棒球服 → 帽子\n",
      "购买球棒与球棒袋,棒球手套,棒球服的条件下有100.00%概率买帽子\n",
      "\n",
      "球棒与球棒袋,棒球手套,帽子 → 棒球服\n",
      "购买球棒与球棒袋,棒球手套,帽子的条件下有100.00%概率买棒球服\n",
      "\n",
      "球棒与球棒袋,棒球服,帽子 → 棒球手套\n",
      "购买球棒与球棒袋,棒球服,帽子的条件下有100.00%概率买棒球手套\n",
      "\n",
      "棒球手套,棒球服,帽子 → 球棒与球棒袋\n",
      "购买棒球手套,棒球服,帽子的条件下有100.00%概率买球棒与球棒袋\n",
      "\n",
      "球棒与球棒袋,棒球手套 → 棒球服,帽子\n",
      "购买球棒与球棒袋,棒球手套的条件下有50.00%概率买棒球服,帽子\n",
      "\n",
      "球棒与球棒袋,棒球服 → 棒球手套,帽子\n",
      "购买球棒与球棒袋,棒球服的条件下有100.00%概率买棒球手套,帽子\n",
      "\n",
      "球棒与球棒袋,帽子 → 棒球手套,棒球服\n",
      "购买球棒与球棒袋,帽子的条件下有100.00%概率买棒球手套,棒球服\n",
      "\n",
      "棒球手套,棒球服 → 球棒与球棒袋,帽子\n",
      "购买棒球手套,棒球服的条件下有50.00%概率买球棒与球棒袋,帽子\n",
      "\n",
      "棒球手套,帽子 → 球棒与球棒袋,棒球服\n",
      "购买棒球手套,帽子的条件下有100.00%概率买球棒与球棒袋,棒球服\n",
      "\n",
      "棒球服,帽子 → 球棒与球棒袋,棒球手套\n",
      "购买棒球服,帽子的条件下有100.00%概率买球棒与球棒袋,棒球手套\n",
      "\n",
      "帽子 → 球棒与球棒袋,棒球手套,棒球服\n",
      "购买帽子的条件下有100.00%概率买球棒与球棒袋,棒球手套,棒球服\n",
      "\n",
      "球棒与球棒袋,棒球手套,软式棒球 → 帽子\n",
      "购买球棒与球棒袋,棒球手套,软式棒球的条件下有100.00%概率买帽子\n",
      "\n",
      "球棒与球棒袋,棒球手套,帽子 → 软式棒球\n",
      "购买球棒与球棒袋,棒球手套,帽子的条件下有100.00%概率买软式棒球\n",
      "\n",
      "球棒与球棒袋,软式棒球,帽子 → 棒球手套\n",
      "购买球棒与球棒袋,软式棒球,帽子的条件下有100.00%概率买棒球手套\n",
      "\n",
      "棒球手套,软式棒球,帽子 → 球棒与球棒袋\n",
      "购买棒球手套,软式棒球,帽子的条件下有100.00%概率买球棒与球棒袋\n",
      "\n",
      "球棒与球棒袋,棒球手套 → 软式棒球,帽子\n",
      "购买球棒与球棒袋,棒球手套的条件下有50.00%概率买软式棒球,帽子\n",
      "\n",
      "球棒与球棒袋,软式棒球 → 棒球手套,帽子\n",
      "购买球棒与球棒袋,软式棒球的条件下有50.00%概率买棒球手套,帽子\n",
      "\n",
      "球棒与球棒袋,帽子 → 棒球手套,软式棒球\n",
      "购买球棒与球棒袋,帽子的条件下有100.00%概率买棒球手套,软式棒球\n",
      "\n",
      "棒球手套,软式棒球 → 球棒与球棒袋,帽子\n",
      "购买棒球手套,软式棒球的条件下有100.00%概率买球棒与球棒袋,帽子\n",
      "\n",
      "棒球手套,帽子 → 球棒与球棒袋,软式棒球\n",
      "购买棒球手套,帽子的条件下有100.00%概率买球棒与球棒袋,软式棒球\n",
      "\n",
      "软式棒球,帽子 → 球棒与球棒袋,棒球手套\n",
      "购买软式棒球,帽子的条件下有100.00%概率买球棒与球棒袋,棒球手套\n",
      "\n",
      "帽子 → 球棒与球棒袋,棒球手套,软式棒球\n",
      "购买帽子的条件下有100.00%概率买球棒与球棒袋,棒球手套,软式棒球\n",
      "\n",
      "球棒与球棒袋,棒球服,软式棒球,帽子 → 三角网架\n",
      "购买球棒与球棒袋,棒球服,软式棒球,帽子的条件下有100.00%概率买三角网架\n",
      "\n",
      "球棒与球棒袋,三角网架,棒球服,帽子 → 软式棒球\n",
      "购买球棒与球棒袋,三角网架,棒球服,帽子的条件下有100.00%概率买软式棒球\n",
      "\n",
      "球棒与球棒袋,三角网架,软式棒球,帽子 → 棒球服\n",
      "购买球棒与球棒袋,三角网架,软式棒球,帽子的条件下有100.00%概率买棒球服\n",
      "\n",
      "球棒与球棒袋,棒球服,软式棒球,三角网架 → 帽子\n",
      "购买球棒与球棒袋,棒球服,软式棒球,三角网架的条件下有100.00%概率买帽子\n",
      "\n",
      "三角网架,棒球服,软式棒球,帽子 → 球棒与球棒袋\n",
      "购买三角网架,棒球服,软式棒球,帽子的条件下有100.00%概率买球棒与球棒袋\n",
      "\n",
      "球棒与球棒袋,棒球服,帽子 → 软式棒球,三角网架\n",
      "购买球棒与球棒袋,棒球服,帽子的条件下有100.00%概率买软式棒球,三角网架\n",
      "\n",
      "球棒与球棒袋,软式棒球,帽子 → 棒球服,三角网架\n",
      "购买球棒与球棒袋,软式棒球,帽子的条件下有100.00%概率买棒球服,三角网架\n",
      "\n",
      "球棒与球棒袋,三角网架,帽子 → 棒球服,软式棒球\n",
      "购买球棒与球棒袋,三角网架,帽子的条件下有100.00%概率买棒球服,软式棒球\n",
      "\n",
      "球棒与球棒袋,棒球服,软式棒球 → 三角网架,帽子\n",
      "购买球棒与球棒袋,棒球服,软式棒球的条件下有100.00%概率买三角网架,帽子\n",
      "\n",
      "球棒与球棒袋,棒球服,三角网架 → 软式棒球,帽子\n",
      "购买球棒与球棒袋,棒球服,三角网架的条件下有100.00%概率买软式棒球,帽子\n",
      "\n",
      "球棒与球棒袋,软式棒球,三角网架 → 棒球服,帽子\n",
      "购买球棒与球棒袋,软式棒球,三角网架的条件下有100.00%概率买棒球服,帽子\n",
      "\n",
      "棒球服,软式棒球,帽子 → 球棒与球棒袋,三角网架\n",
      "购买棒球服,软式棒球,帽子的条件下有100.00%概率买球棒与球棒袋,三角网架\n",
      "\n",
      "三角网架,棒球服,帽子 → 球棒与球棒袋,软式棒球\n",
      "购买三角网架,棒球服,帽子的条件下有100.00%概率买球棒与球棒袋,软式棒球\n",
      "\n",
      "三角网架,软式棒球,帽子 → 球棒与球棒袋,棒球服\n",
      "购买三角网架,软式棒球,帽子的条件下有100.00%概率买球棒与球棒袋,棒球服\n",
      "\n",
      "棒球服,软式棒球,三角网架 → 球棒与球棒袋,帽子\n",
      "购买棒球服,软式棒球,三角网架的条件下有50.00%概率买球棒与球棒袋,帽子\n",
      "\n",
      "球棒与球棒袋,帽子 → 棒球服,软式棒球,三角网架\n",
      "购买球棒与球棒袋,帽子的条件下有100.00%概率买棒球服,软式棒球,三角网架\n",
      "\n",
      "球棒与球棒袋,棒球服 → 三角网架,软式棒球,帽子\n",
      "购买球棒与球棒袋,棒球服的条件下有100.00%概率买三角网架,软式棒球,帽子\n",
      "\n",
      "球棒与球棒袋,软式棒球 → 三角网架,棒球服,帽子\n",
      "购买球棒与球棒袋,软式棒球的条件下有50.00%概率买三角网架,棒球服,帽子\n",
      "\n",
      "球棒与球棒袋,三角网架 → 棒球服,软式棒球,帽子\n",
      "购买球棒与球棒袋,三角网架的条件下有100.00%概率买棒球服,软式棒球,帽子\n",
      "\n",
      "棒球服,帽子 → 球棒与球棒袋,软式棒球,三角网架\n",
      "购买棒球服,帽子的条件下有100.00%概率买球棒与球棒袋,软式棒球,三角网架\n",
      "\n",
      "软式棒球,帽子 → 球棒与球棒袋,棒球服,三角网架\n",
      "购买软式棒球,帽子的条件下有100.00%概率买球棒与球棒袋,棒球服,三角网架\n",
      "\n",
      "三角网架,帽子 → 球棒与球棒袋,棒球服,软式棒球\n",
      "购买三角网架,帽子的条件下有100.00%概率买球棒与球棒袋,棒球服,软式棒球\n",
      "\n",
      "棒球服,软式棒球 → 球棒与球棒袋,三角网架,帽子\n",
      "购买棒球服,软式棒球的条件下有50.00%概率买球棒与球棒袋,三角网架,帽子\n",
      "\n",
      "棒球服,三角网架 → 球棒与球棒袋,软式棒球,帽子\n",
      "购买棒球服,三角网架的条件下有50.00%概率买球棒与球棒袋,软式棒球,帽子\n",
      "\n",
      "软式棒球,三角网架 → 球棒与球棒袋,棒球服,帽子\n",
      "购买软式棒球,三角网架的条件下有50.00%概率买球棒与球棒袋,棒球服,帽子\n",
      "\n",
      "帽子 → 球棒与球棒袋,棒球服,软式棒球,三角网架\n",
      "购买帽子的条件下有100.00%概率买球棒与球棒袋,棒球服,软式棒球,三角网架\n",
      "\n",
      "三角网架 → 球棒与球棒袋,棒球服,软式棒球,帽子\n",
      "购买三角网架的条件下有33.33%概率买球棒与球棒袋,棒球服,软式棒球,帽子\n",
      "\n",
      "棒球手套,棒球服,软式棒球,帽子 → 三角网架\n",
      "购买棒球手套,棒球服,软式棒球,帽子的条件下有100.00%概率买三角网架\n",
      "\n",
      "三角网架,棒球服,软式棒球,帽子 → 棒球手套\n",
      "购买三角网架,棒球服,软式棒球,帽子的条件下有100.00%概率买棒球手套\n",
      "\n",
      "棒球手套,三角网架,棒球服,帽子 → 软式棒球\n",
      "购买棒球手套,三角网架,棒球服,帽子的条件下有100.00%概率买软式棒球\n",
      "\n",
      "棒球手套,三角网架,软式棒球,帽子 → 棒球服\n",
      "购买棒球手套,三角网架,软式棒球,帽子的条件下有100.00%概率买棒球服\n",
      "\n",
      "棒球手套,棒球服,软式棒球,三角网架 → 帽子\n",
      "购买棒球手套,棒球服,软式棒球,三角网架的条件下有100.00%概率买帽子\n",
      "\n",
      "棒球服,软式棒球,帽子 → 棒球手套,三角网架\n",
      "购买棒球服,软式棒球,帽子的条件下有100.00%概率买棒球手套,三角网架\n",
      "\n",
      "棒球手套,棒球服,帽子 → 软式棒球,三角网架\n",
      "购买棒球手套,棒球服,帽子的条件下有100.00%概率买软式棒球,三角网架\n",
      "\n",
      "三角网架,棒球服,帽子 → 棒球手套,软式棒球\n",
      "购买三角网架,棒球服,帽子的条件下有100.00%概率买棒球手套,软式棒球\n",
      "\n",
      "棒球手套,软式棒球,帽子 → 棒球服,三角网架\n",
      "购买棒球手套,软式棒球,帽子的条件下有100.00%概率买棒球服,三角网架\n",
      "\n",
      "三角网架,软式棒球,帽子 → 棒球手套,棒球服\n",
      "购买三角网架,软式棒球,帽子的条件下有100.00%概率买棒球手套,棒球服\n",
      "\n",
      "棒球手套,三角网架,帽子 → 棒球服,软式棒球\n",
      "购买棒球手套,三角网架,帽子的条件下有100.00%概率买棒球服,软式棒球\n",
      "\n",
      "棒球手套,棒球服,软式棒球 → 三角网架,帽子\n",
      "购买棒球手套,棒球服,软式棒球的条件下有100.00%概率买三角网架,帽子\n",
      "\n",
      "棒球服,软式棒球,三角网架 → 棒球手套,帽子\n",
      "购买棒球服,软式棒球,三角网架的条件下有50.00%概率买棒球手套,帽子\n",
      "\n",
      "棒球手套,棒球服,三角网架 → 软式棒球,帽子\n",
      "购买棒球手套,棒球服,三角网架的条件下有100.00%概率买软式棒球,帽子\n",
      "\n",
      "棒球手套,软式棒球,三角网架 → 棒球服,帽子\n",
      "购买棒球手套,软式棒球,三角网架的条件下有100.00%概率买棒球服,帽子\n",
      "\n",
      "棒球服,帽子 → 棒球手套,软式棒球,三角网架\n",
      "购买棒球服,帽子的条件下有100.00%概率买棒球手套,软式棒球,三角网架\n",
      "\n",
      "软式棒球,帽子 → 棒球手套,棒球服,三角网架\n",
      "购买软式棒球,帽子的条件下有100.00%概率买棒球手套,棒球服,三角网架\n",
      "\n",
      "棒球手套,帽子 → 棒球服,软式棒球,三角网架\n",
      "购买棒球手套,帽子的条件下有100.00%概率买棒球服,软式棒球,三角网架\n",
      "\n",
      "三角网架,帽子 → 棒球手套,棒球服,软式棒球\n",
      "购买三角网架,帽子的条件下有100.00%概率买棒球手套,棒球服,软式棒球\n",
      "\n",
      "棒球服,软式棒球 → 棒球手套,三角网架,帽子\n",
      "购买棒球服,软式棒球的条件下有50.00%概率买棒球手套,三角网架,帽子\n",
      "\n",
      "棒球手套,棒球服 → 三角网架,软式棒球,帽子\n",
      "购买棒球手套,棒球服的条件下有50.00%概率买三角网架,软式棒球,帽子\n",
      "\n",
      "棒球服,三角网架 → 棒球手套,软式棒球,帽子\n",
      "购买棒球服,三角网架的条件下有50.00%概率买棒球手套,软式棒球,帽子\n",
      "\n",
      "棒球手套,软式棒球 → 三角网架,棒球服,帽子\n",
      "购买棒球手套,软式棒球的条件下有100.00%概率买三角网架,棒球服,帽子\n",
      "\n",
      "软式棒球,三角网架 → 棒球手套,棒球服,帽子\n",
      "购买软式棒球,三角网架的条件下有50.00%概率买棒球手套,棒球服,帽子\n",
      "\n",
      "棒球手套,三角网架 → 棒球服,软式棒球,帽子\n",
      "购买棒球手套,三角网架的条件下有50.00%概率买棒球服,软式棒球,帽子\n",
      "\n",
      "帽子 → 棒球手套,棒球服,软式棒球,三角网架\n",
      "购买帽子的条件下有100.00%概率买棒球手套,棒球服,软式棒球,三角网架\n",
      "\n",
      "三角网架 → 棒球手套,棒球服,软式棒球,帽子\n",
      "购买三角网架的条件下有33.33%概率买棒球手套,棒球服,软式棒球,帽子\n",
      "\n",
      "球棒与球棒袋,棒球手套,棒球服,帽子 → 三角网架\n",
      "购买球棒与球棒袋,棒球手套,棒球服,帽子的条件下有100.00%概率买三角网架\n",
      "\n",
      "球棒与球棒袋,三角网架,棒球服,帽子 → 棒球手套\n",
      "购买球棒与球棒袋,三角网架,棒球服,帽子的条件下有100.00%概率买棒球手套\n",
      "\n",
      "球棒与球棒袋,棒球手套,三角网架,帽子 → 棒球服\n",
      "购买球棒与球棒袋,棒球手套,三角网架,帽子的条件下有100.00%概率买棒球服\n",
      "\n",
      "球棒与球棒袋,棒球手套,棒球服,三角网架 → 帽子\n",
      "购买球棒与球棒袋,棒球手套,棒球服,三角网架的条件下有100.00%概率买帽子\n",
      "\n",
      "棒球手套,三角网架,棒球服,帽子 → 球棒与球棒袋\n",
      "购买棒球手套,三角网架,棒球服,帽子的条件下有100.00%概率买球棒与球棒袋\n",
      "\n",
      "球棒与球棒袋,棒球服,帽子 → 棒球手套,三角网架\n",
      "购买球棒与球棒袋,棒球服,帽子的条件下有100.00%概率买棒球手套,三角网架\n",
      "\n",
      "球棒与球棒袋,棒球手套,帽子 → 棒球服,三角网架\n",
      "购买球棒与球棒袋,棒球手套,帽子的条件下有100.00%概率买棒球服,三角网架\n",
      "\n",
      "球棒与球棒袋,三角网架,帽子 → 棒球手套,棒球服\n",
      "购买球棒与球棒袋,三角网架,帽子的条件下有100.00%概率买棒球手套,棒球服\n",
      "\n",
      "球棒与球棒袋,棒球手套,棒球服 → 三角网架,帽子\n",
      "购买球棒与球棒袋,棒球手套,棒球服的条件下有100.00%概率买三角网架,帽子\n",
      "\n",
      "球棒与球棒袋,棒球服,三角网架 → 棒球手套,帽子\n",
      "购买球棒与球棒袋,棒球服,三角网架的条件下有100.00%概率买棒球手套,帽子\n",
      "\n",
      "球棒与球棒袋,棒球手套,三角网架 → 棒球服,帽子\n",
      "购买球棒与球棒袋,棒球手套,三角网架的条件下有100.00%概率买棒球服,帽子\n",
      "\n",
      "棒球手套,棒球服,帽子 → 球棒与球棒袋,三角网架\n",
      "购买棒球手套,棒球服,帽子的条件下有100.00%概率买球棒与球棒袋,三角网架\n",
      "\n",
      "三角网架,棒球服,帽子 → 球棒与球棒袋,棒球手套\n",
      "购买三角网架,棒球服,帽子的条件下有100.00%概率买球棒与球棒袋,棒球手套\n",
      "\n",
      "棒球手套,三角网架,帽子 → 球棒与球棒袋,棒球服\n",
      "购买棒球手套,三角网架,帽子的条件下有100.00%概率买球棒与球棒袋,棒球服\n",
      "\n",
      "棒球手套,棒球服,三角网架 → 球棒与球棒袋,帽子\n",
      "购买棒球手套,棒球服,三角网架的条件下有100.00%概率买球棒与球棒袋,帽子\n",
      "\n",
      "球棒与球棒袋,帽子 → 棒球手套,棒球服,三角网架\n",
      "购买球棒与球棒袋,帽子的条件下有100.00%概率买棒球手套,棒球服,三角网架\n",
      "\n",
      "球棒与球棒袋,棒球服 → 棒球手套,三角网架,帽子\n",
      "购买球棒与球棒袋,棒球服的条件下有100.00%概率买棒球手套,三角网架,帽子\n",
      "\n",
      "球棒与球棒袋,棒球手套 → 三角网架,棒球服,帽子\n",
      "购买球棒与球棒袋,棒球手套的条件下有50.00%概率买三角网架,棒球服,帽子\n",
      "\n",
      "球棒与球棒袋,三角网架 → 棒球手套,棒球服,帽子\n",
      "购买球棒与球棒袋,三角网架的条件下有100.00%概率买棒球手套,棒球服,帽子\n",
      "\n",
      "棒球服,帽子 → 球棒与球棒袋,棒球手套,三角网架\n",
      "购买棒球服,帽子的条件下有100.00%概率买球棒与球棒袋,棒球手套,三角网架\n",
      "\n",
      "棒球手套,帽子 → 球棒与球棒袋,棒球服,三角网架\n",
      "购买棒球手套,帽子的条件下有100.00%概率买球棒与球棒袋,棒球服,三角网架\n",
      "\n",
      "三角网架,帽子 → 球棒与球棒袋,棒球手套,棒球服\n",
      "购买三角网架,帽子的条件下有100.00%概率买球棒与球棒袋,棒球手套,棒球服\n",
      "\n",
      "棒球手套,棒球服 → 球棒与球棒袋,三角网架,帽子\n",
      "购买棒球手套,棒球服的条件下有50.00%概率买球棒与球棒袋,三角网架,帽子\n",
      "\n",
      "棒球服,三角网架 → 球棒与球棒袋,棒球手套,帽子\n",
      "购买棒球服,三角网架的条件下有50.00%概率买球棒与球棒袋,棒球手套,帽子\n",
      "\n",
      "棒球手套,三角网架 → 球棒与球棒袋,棒球服,帽子\n",
      "购买棒球手套,三角网架的条件下有50.00%概率买球棒与球棒袋,棒球服,帽子\n",
      "\n",
      "帽子 → 球棒与球棒袋,棒球手套,棒球服,三角网架\n",
      "购买帽子的条件下有100.00%概率买球棒与球棒袋,棒球手套,棒球服,三角网架\n",
      "\n",
      "三角网架 → 球棒与球棒袋,棒球手套,棒球服,帽子\n",
      "购买三角网架的条件下有33.33%概率买球棒与球棒袋,棒球手套,棒球服,帽子\n",
      "\n",
      "球棒与球棒袋,棒球手套,软式棒球,帽子 → 三角网架\n",
      "购买球棒与球棒袋,棒球手套,软式棒球,帽子的条件下有100.00%概率买三角网架\n",
      "\n",
      "球棒与球棒袋,三角网架,软式棒球,帽子 → 棒球手套\n",
      "购买球棒与球棒袋,三角网架,软式棒球,帽子的条件下有100.00%概率买棒球手套\n",
      "\n",
      "球棒与球棒袋,棒球手套,三角网架,帽子 → 软式棒球\n",
      "购买球棒与球棒袋,棒球手套,三角网架,帽子的条件下有100.00%概率买软式棒球\n",
      "\n",
      "球棒与球棒袋,棒球手套,软式棒球,三角网架 → 帽子\n",
      "购买球棒与球棒袋,棒球手套,软式棒球,三角网架的条件下有100.00%概率买帽子\n",
      "\n",
      "棒球手套,三角网架,软式棒球,帽子 → 球棒与球棒袋\n",
      "购买棒球手套,三角网架,软式棒球,帽子的条件下有100.00%概率买球棒与球棒袋\n",
      "\n",
      "球棒与球棒袋,软式棒球,帽子 → 棒球手套,三角网架\n",
      "购买球棒与球棒袋,软式棒球,帽子的条件下有100.00%概率买棒球手套,三角网架\n",
      "\n",
      "球棒与球棒袋,棒球手套,帽子 → 软式棒球,三角网架\n",
      "购买球棒与球棒袋,棒球手套,帽子的条件下有100.00%概率买软式棒球,三角网架\n",
      "\n",
      "球棒与球棒袋,三角网架,帽子 → 棒球手套,软式棒球\n",
      "购买球棒与球棒袋,三角网架,帽子的条件下有100.00%概率买棒球手套,软式棒球\n",
      "\n",
      "球棒与球棒袋,棒球手套,软式棒球 → 三角网架,帽子\n",
      "购买球棒与球棒袋,棒球手套,软式棒球的条件下有100.00%概率买三角网架,帽子\n",
      "\n",
      "球棒与球棒袋,软式棒球,三角网架 → 棒球手套,帽子\n",
      "购买球棒与球棒袋,软式棒球,三角网架的条件下有100.00%概率买棒球手套,帽子\n",
      "\n",
      "球棒与球棒袋,棒球手套,三角网架 → 软式棒球,帽子\n",
      "购买球棒与球棒袋,棒球手套,三角网架的条件下有100.00%概率买软式棒球,帽子\n",
      "\n",
      "棒球手套,软式棒球,帽子 → 球棒与球棒袋,三角网架\n",
      "购买棒球手套,软式棒球,帽子的条件下有100.00%概率买球棒与球棒袋,三角网架\n",
      "\n",
      "三角网架,软式棒球,帽子 → 球棒与球棒袋,棒球手套\n",
      "购买三角网架,软式棒球,帽子的条件下有100.00%概率买球棒与球棒袋,棒球手套\n",
      "\n",
      "棒球手套,三角网架,帽子 → 球棒与球棒袋,软式棒球\n",
      "购买棒球手套,三角网架,帽子的条件下有100.00%概率买球棒与球棒袋,软式棒球\n",
      "\n",
      "棒球手套,软式棒球,三角网架 → 球棒与球棒袋,帽子\n",
      "购买棒球手套,软式棒球,三角网架的条件下有100.00%概率买球棒与球棒袋,帽子\n",
      "\n",
      "球棒与球棒袋,帽子 → 棒球手套,软式棒球,三角网架\n",
      "购买球棒与球棒袋,帽子的条件下有100.00%概率买棒球手套,软式棒球,三角网架\n",
      "\n",
      "球棒与球棒袋,软式棒球 → 棒球手套,三角网架,帽子\n",
      "购买球棒与球棒袋,软式棒球的条件下有50.00%概率买棒球手套,三角网架,帽子\n",
      "\n",
      "球棒与球棒袋,棒球手套 → 三角网架,软式棒球,帽子\n",
      "购买球棒与球棒袋,棒球手套的条件下有50.00%概率买三角网架,软式棒球,帽子\n",
      "\n",
      "球棒与球棒袋,三角网架 → 棒球手套,软式棒球,帽子\n",
      "购买球棒与球棒袋,三角网架的条件下有100.00%概率买棒球手套,软式棒球,帽子\n",
      "\n",
      "软式棒球,帽子 → 球棒与球棒袋,棒球手套,三角网架\n",
      "购买软式棒球,帽子的条件下有100.00%概率买球棒与球棒袋,棒球手套,三角网架\n",
      "\n",
      "棒球手套,帽子 → 球棒与球棒袋,软式棒球,三角网架\n",
      "购买棒球手套,帽子的条件下有100.00%概率买球棒与球棒袋,软式棒球,三角网架\n",
      "\n",
      "三角网架,帽子 → 球棒与球棒袋,棒球手套,软式棒球\n",
      "购买三角网架,帽子的条件下有100.00%概率买球棒与球棒袋,棒球手套,软式棒球\n",
      "\n",
      "棒球手套,软式棒球 → 球棒与球棒袋,三角网架,帽子\n",
      "购买棒球手套,软式棒球的条件下有100.00%概率买球棒与球棒袋,三角网架,帽子\n",
      "\n",
      "软式棒球,三角网架 → 球棒与球棒袋,棒球手套,帽子\n",
      "购买软式棒球,三角网架的条件下有50.00%概率买球棒与球棒袋,棒球手套,帽子\n",
      "\n",
      "棒球手套,三角网架 → 球棒与球棒袋,软式棒球,帽子\n",
      "购买棒球手套,三角网架的条件下有50.00%概率买球棒与球棒袋,软式棒球,帽子\n",
      "\n",
      "帽子 → 球棒与球棒袋,棒球手套,软式棒球,三角网架\n",
      "购买帽子的条件下有100.00%概率买球棒与球棒袋,棒球手套,软式棒球,三角网架\n",
      "\n",
      "三角网架 → 球棒与球棒袋,棒球手套,软式棒球,帽子\n",
      "购买三角网架的条件下有33.33%概率买球棒与球棒袋,棒球手套,软式棒球,帽子\n",
      "\n",
      "球棒与球棒袋,棒球服,软式棒球,帽子 → 棒球手套\n",
      "购买球棒与球棒袋,棒球服,软式棒球,帽子的条件下有100.00%概率买棒球手套\n",
      "\n",
      "球棒与球棒袋,棒球手套,棒球服,帽子 → 软式棒球\n",
      "购买球棒与球棒袋,棒球手套,棒球服,帽子的条件下有100.00%概率买软式棒球\n",
      "\n",
      "球棒与球棒袋,棒球手套,软式棒球,帽子 → 棒球服\n",
      "购买球棒与球棒袋,棒球手套,软式棒球,帽子的条件下有100.00%概率买棒球服\n",
      "\n",
      "球棒与球棒袋,棒球手套,棒球服,软式棒球 → 帽子\n",
      "购买球棒与球棒袋,棒球手套,棒球服,软式棒球的条件下有100.00%概率买帽子\n",
      "\n",
      "棒球手套,棒球服,软式棒球,帽子 → 球棒与球棒袋\n",
      "购买棒球手套,棒球服,软式棒球,帽子的条件下有100.00%概率买球棒与球棒袋\n",
      "\n",
      "球棒与球棒袋,棒球服,帽子 → 棒球手套,软式棒球\n",
      "购买球棒与球棒袋,棒球服,帽子的条件下有100.00%概率买棒球手套,软式棒球\n",
      "\n",
      "球棒与球棒袋,软式棒球,帽子 → 棒球手套,棒球服\n",
      "购买球棒与球棒袋,软式棒球,帽子的条件下有100.00%概率买棒球手套,棒球服\n",
      "\n",
      "球棒与球棒袋,棒球手套,帽子 → 棒球服,软式棒球\n",
      "购买球棒与球棒袋,棒球手套,帽子的条件下有100.00%概率买棒球服,软式棒球\n",
      "\n",
      "球棒与球棒袋,棒球服,软式棒球 → 棒球手套,帽子\n",
      "购买球棒与球棒袋,棒球服,软式棒球的条件下有100.00%概率买棒球手套,帽子\n",
      "\n",
      "球棒与球棒袋,棒球手套,棒球服 → 软式棒球,帽子\n",
      "购买球棒与球棒袋,棒球手套,棒球服的条件下有100.00%概率买软式棒球,帽子\n",
      "\n",
      "球棒与球棒袋,棒球手套,软式棒球 → 棒球服,帽子\n",
      "购买球棒与球棒袋,棒球手套,软式棒球的条件下有100.00%概率买棒球服,帽子\n",
      "\n",
      "棒球服,软式棒球,帽子 → 球棒与球棒袋,棒球手套\n",
      "购买棒球服,软式棒球,帽子的条件下有100.00%概率买球棒与球棒袋,棒球手套\n",
      "\n",
      "棒球手套,棒球服,帽子 → 球棒与球棒袋,软式棒球\n",
      "购买棒球手套,棒球服,帽子的条件下有100.00%概率买球棒与球棒袋,软式棒球\n",
      "\n",
      "棒球手套,软式棒球,帽子 → 球棒与球棒袋,棒球服\n",
      "购买棒球手套,软式棒球,帽子的条件下有100.00%概率买球棒与球棒袋,棒球服\n",
      "\n",
      "棒球手套,棒球服,软式棒球 → 球棒与球棒袋,帽子\n",
      "购买棒球手套,棒球服,软式棒球的条件下有100.00%概率买球棒与球棒袋,帽子\n",
      "\n",
      "球棒与球棒袋,帽子 → 棒球手套,棒球服,软式棒球\n",
      "购买球棒与球棒袋,帽子的条件下有100.00%概率买棒球手套,棒球服,软式棒球\n",
      "\n",
      "球棒与球棒袋,棒球服 → 棒球手套,软式棒球,帽子\n",
      "购买球棒与球棒袋,棒球服的条件下有100.00%概率买棒球手套,软式棒球,帽子\n",
      "\n",
      "球棒与球棒袋,软式棒球 → 棒球手套,棒球服,帽子\n",
      "购买球棒与球棒袋,软式棒球的条件下有50.00%概率买棒球手套,棒球服,帽子\n",
      "\n",
      "球棒与球棒袋,棒球手套 → 棒球服,软式棒球,帽子\n",
      "购买球棒与球棒袋,棒球手套的条件下有50.00%概率买棒球服,软式棒球,帽子\n",
      "\n",
      "棒球服,帽子 → 球棒与球棒袋,棒球手套,软式棒球\n",
      "购买棒球服,帽子的条件下有100.00%概率买球棒与球棒袋,棒球手套,软式棒球\n",
      "\n",
      "软式棒球,帽子 → 球棒与球棒袋,棒球手套,棒球服\n",
      "购买软式棒球,帽子的条件下有100.00%概率买球棒与球棒袋,棒球手套,棒球服\n",
      "\n",
      "棒球手套,帽子 → 球棒与球棒袋,棒球服,软式棒球\n",
      "购买棒球手套,帽子的条件下有100.00%概率买球棒与球棒袋,棒球服,软式棒球\n",
      "\n",
      "棒球服,软式棒球 → 球棒与球棒袋,棒球手套,帽子\n",
      "购买棒球服,软式棒球的条件下有50.00%概率买球棒与球棒袋,棒球手套,帽子\n",
      "\n",
      "棒球手套,棒球服 → 球棒与球棒袋,软式棒球,帽子\n",
      "购买棒球手套,棒球服的条件下有50.00%概率买球棒与球棒袋,软式棒球,帽子\n",
      "\n",
      "棒球手套,软式棒球 → 球棒与球棒袋,棒球服,帽子\n",
      "购买棒球手套,软式棒球的条件下有100.00%概率买球棒与球棒袋,棒球服,帽子\n",
      "\n",
      "帽子 → 球棒与球棒袋,棒球手套,棒球服,软式棒球\n",
      "购买帽子的条件下有100.00%概率买球棒与球棒袋,棒球手套,棒球服,软式棒球\n",
      "\n",
      "球棒与球棒袋,帽子,棒球服,软式棒球,棒球手套 → 三角网架\n",
      "购买球棒与球棒袋,帽子,棒球服,软式棒球,棒球手套的条件下有100.00%概率买三角网架\n",
      "\n",
      "球棒与球棒袋,帽子,棒球服,软式棒球,三角网架 → 棒球手套\n",
      "购买球棒与球棒袋,帽子,棒球服,软式棒球,三角网架的条件下有100.00%概率买棒球手套\n",
      "\n",
      "球棒与球棒袋,帽子,棒球服,棒球手套,三角网架 → 软式棒球\n",
      "购买球棒与球棒袋,帽子,棒球服,棒球手套,三角网架的条件下有100.00%概率买软式棒球\n",
      "\n",
      "球棒与球棒袋,帽子,软式棒球,棒球手套,三角网架 → 棒球服\n",
      "购买球棒与球棒袋,帽子,软式棒球,棒球手套,三角网架的条件下有100.00%概率买棒球服\n",
      "\n",
      "球棒与球棒袋,棒球服,软式棒球,棒球手套,三角网架 → 帽子\n",
      "购买球棒与球棒袋,棒球服,软式棒球,棒球手套,三角网架的条件下有100.00%概率买帽子\n",
      "\n",
      "帽子,棒球服,软式棒球,棒球手套,三角网架 → 球棒与球棒袋\n",
      "购买帽子,棒球服,软式棒球,棒球手套,三角网架的条件下有100.00%概率买球棒与球棒袋\n",
      "\n",
      "球棒与球棒袋,棒球服,软式棒球,帽子 → 棒球手套,三角网架\n",
      "购买球棒与球棒袋,棒球服,软式棒球,帽子的条件下有100.00%概率买棒球手套,三角网架\n",
      "\n",
      "球棒与球棒袋,棒球手套,棒球服,帽子 → 软式棒球,三角网架\n",
      "购买球棒与球棒袋,棒球手套,棒球服,帽子的条件下有100.00%概率买软式棒球,三角网架\n",
      "\n",
      "球棒与球棒袋,三角网架,棒球服,帽子 → 棒球手套,软式棒球\n",
      "购买球棒与球棒袋,三角网架,棒球服,帽子的条件下有100.00%概率买棒球手套,软式棒球\n",
      "\n",
      "球棒与球棒袋,棒球手套,软式棒球,帽子 → 棒球服,三角网架\n",
      "购买球棒与球棒袋,棒球手套,软式棒球,帽子的条件下有100.00%概率买棒球服,三角网架\n",
      "\n",
      "球棒与球棒袋,三角网架,软式棒球,帽子 → 棒球手套,棒球服\n",
      "购买球棒与球棒袋,三角网架,软式棒球,帽子的条件下有100.00%概率买棒球手套,棒球服\n",
      "\n",
      "球棒与球棒袋,棒球手套,三角网架,帽子 → 棒球服,软式棒球\n",
      "购买球棒与球棒袋,棒球手套,三角网架,帽子的条件下有100.00%概率买棒球服,软式棒球\n",
      "\n",
      "球棒与球棒袋,棒球手套,棒球服,软式棒球 → 三角网架,帽子\n",
      "购买球棒与球棒袋,棒球手套,棒球服,软式棒球的条件下有100.00%概率买三角网架,帽子\n",
      "\n",
      "球棒与球棒袋,棒球服,软式棒球,三角网架 → 棒球手套,帽子\n",
      "购买球棒与球棒袋,棒球服,软式棒球,三角网架的条件下有100.00%概率买棒球手套,帽子\n",
      "\n",
      "球棒与球棒袋,棒球手套,棒球服,三角网架 → 软式棒球,帽子\n",
      "购买球棒与球棒袋,棒球手套,棒球服,三角网架的条件下有100.00%概率买软式棒球,帽子\n",
      "\n",
      "球棒与球棒袋,棒球手套,软式棒球,三角网架 → 棒球服,帽子\n",
      "购买球棒与球棒袋,棒球手套,软式棒球,三角网架的条件下有100.00%概率买棒球服,帽子\n",
      "\n",
      "棒球手套,棒球服,软式棒球,帽子 → 球棒与球棒袋,三角网架\n",
      "购买棒球手套,棒球服,软式棒球,帽子的条件下有100.00%概率买球棒与球棒袋,三角网架\n",
      "\n",
      "三角网架,棒球服,软式棒球,帽子 → 球棒与球棒袋,棒球手套\n",
      "购买三角网架,棒球服,软式棒球,帽子的条件下有100.00%概率买球棒与球棒袋,棒球手套\n",
      "\n",
      "棒球手套,三角网架,棒球服,帽子 → 球棒与球棒袋,软式棒球\n",
      "购买棒球手套,三角网架,棒球服,帽子的条件下有100.00%概率买球棒与球棒袋,软式棒球\n",
      "\n",
      "棒球手套,三角网架,软式棒球,帽子 → 球棒与球棒袋,棒球服\n",
      "购买棒球手套,三角网架,软式棒球,帽子的条件下有100.00%概率买球棒与球棒袋,棒球服\n",
      "\n",
      "棒球手套,棒球服,软式棒球,三角网架 → 球棒与球棒袋,帽子\n",
      "购买棒球手套,棒球服,软式棒球,三角网架的条件下有100.00%概率买球棒与球棒袋,帽子\n",
      "\n",
      "球棒与球棒袋,棒球服,帽子 → 棒球手套,软式棒球,三角网架\n",
      "购买球棒与球棒袋,棒球服,帽子的条件下有100.00%概率买棒球手套,软式棒球,三角网架\n",
      "\n",
      "球棒与球棒袋,软式棒球,帽子 → 棒球手套,棒球服,三角网架\n",
      "购买球棒与球棒袋,软式棒球,帽子的条件下有100.00%概率买棒球手套,棒球服,三角网架\n",
      "\n",
      "球棒与球棒袋,棒球手套,帽子 → 棒球服,软式棒球,三角网架\n",
      "购买球棒与球棒袋,棒球手套,帽子的条件下有100.00%概率买棒球服,软式棒球,三角网架\n",
      "\n",
      "球棒与球棒袋,三角网架,帽子 → 棒球手套,棒球服,软式棒球\n",
      "购买球棒与球棒袋,三角网架,帽子的条件下有100.00%概率买棒球手套,棒球服,软式棒球\n",
      "\n",
      "球棒与球棒袋,棒球服,软式棒球 → 棒球手套,三角网架,帽子\n",
      "购买球棒与球棒袋,棒球服,软式棒球的条件下有100.00%概率买棒球手套,三角网架,帽子\n",
      "\n",
      "球棒与球棒袋,棒球手套,棒球服 → 三角网架,软式棒球,帽子\n",
      "购买球棒与球棒袋,棒球手套,棒球服的条件下有100.00%概率买三角网架,软式棒球,帽子\n",
      "\n",
      "球棒与球棒袋,棒球服,三角网架 → 棒球手套,软式棒球,帽子\n",
      "购买球棒与球棒袋,棒球服,三角网架的条件下有100.00%概率买棒球手套,软式棒球,帽子\n",
      "\n",
      "球棒与球棒袋,棒球手套,软式棒球 → 三角网架,棒球服,帽子\n",
      "购买球棒与球棒袋,棒球手套,软式棒球的条件下有100.00%概率买三角网架,棒球服,帽子\n",
      "\n",
      "球棒与球棒袋,软式棒球,三角网架 → 棒球手套,棒球服,帽子\n",
      "购买球棒与球棒袋,软式棒球,三角网架的条件下有100.00%概率买棒球手套,棒球服,帽子\n",
      "\n",
      "球棒与球棒袋,棒球手套,三角网架 → 棒球服,软式棒球,帽子\n",
      "购买球棒与球棒袋,棒球手套,三角网架的条件下有100.00%概率买棒球服,软式棒球,帽子\n",
      "\n",
      "棒球服,软式棒球,帽子 → 球棒与球棒袋,棒球手套,三角网架\n",
      "购买棒球服,软式棒球,帽子的条件下有100.00%概率买球棒与球棒袋,棒球手套,三角网架\n",
      "\n",
      "棒球手套,棒球服,帽子 → 球棒与球棒袋,软式棒球,三角网架\n",
      "购买棒球手套,棒球服,帽子的条件下有100.00%概率买球棒与球棒袋,软式棒球,三角网架\n",
      "\n",
      "三角网架,棒球服,帽子 → 球棒与球棒袋,棒球手套,软式棒球\n",
      "购买三角网架,棒球服,帽子的条件下有100.00%概率买球棒与球棒袋,棒球手套,软式棒球\n",
      "\n",
      "棒球手套,软式棒球,帽子 → 球棒与球棒袋,棒球服,三角网架\n",
      "购买棒球手套,软式棒球,帽子的条件下有100.00%概率买球棒与球棒袋,棒球服,三角网架\n",
      "\n",
      "三角网架,软式棒球,帽子 → 球棒与球棒袋,棒球手套,棒球服\n",
      "购买三角网架,软式棒球,帽子的条件下有100.00%概率买球棒与球棒袋,棒球手套,棒球服\n",
      "\n",
      "棒球手套,三角网架,帽子 → 球棒与球棒袋,棒球服,软式棒球\n",
      "购买棒球手套,三角网架,帽子的条件下有100.00%概率买球棒与球棒袋,棒球服,软式棒球\n",
      "\n",
      "棒球手套,棒球服,软式棒球 → 球棒与球棒袋,三角网架,帽子\n",
      "购买棒球手套,棒球服,软式棒球的条件下有100.00%概率买球棒与球棒袋,三角网架,帽子\n",
      "\n",
      "棒球服,软式棒球,三角网架 → 球棒与球棒袋,棒球手套,帽子\n",
      "购买棒球服,软式棒球,三角网架的条件下有50.00%概率买球棒与球棒袋,棒球手套,帽子\n",
      "\n",
      "棒球手套,棒球服,三角网架 → 球棒与球棒袋,软式棒球,帽子\n",
      "购买棒球手套,棒球服,三角网架的条件下有100.00%概率买球棒与球棒袋,软式棒球,帽子\n",
      "\n",
      "棒球手套,软式棒球,三角网架 → 球棒与球棒袋,棒球服,帽子\n",
      "购买棒球手套,软式棒球,三角网架的条件下有100.00%概率买球棒与球棒袋,棒球服,帽子\n",
      "\n",
      "球棒与球棒袋,帽子 → 棒球手套,棒球服,软式棒球,三角网架\n",
      "购买球棒与球棒袋,帽子的条件下有100.00%概率买棒球手套,棒球服,软式棒球,三角网架\n",
      "\n",
      "球棒与球棒袋,棒球服 → 棒球手套,三角网架,软式棒球,帽子\n",
      "购买球棒与球棒袋,棒球服的条件下有100.00%概率买棒球手套,三角网架,软式棒球,帽子\n",
      "\n",
      "球棒与球棒袋,软式棒球 → 棒球手套,三角网架,棒球服,帽子\n",
      "购买球棒与球棒袋,软式棒球的条件下有50.00%概率买棒球手套,三角网架,棒球服,帽子\n",
      "\n",
      "球棒与球棒袋,棒球手套 → 三角网架,棒球服,软式棒球,帽子\n",
      "购买球棒与球棒袋,棒球手套的条件下有50.00%概率买三角网架,棒球服,软式棒球,帽子\n",
      "\n",
      "球棒与球棒袋,三角网架 → 棒球手套,棒球服,软式棒球,帽子\n",
      "购买球棒与球棒袋,三角网架的条件下有100.00%概率买棒球手套,棒球服,软式棒球,帽子\n",
      "\n",
      "棒球服,帽子 → 球棒与球棒袋,棒球手套,软式棒球,三角网架\n",
      "购买棒球服,帽子的条件下有100.00%概率买球棒与球棒袋,棒球手套,软式棒球,三角网架\n",
      "\n",
      "软式棒球,帽子 → 球棒与球棒袋,棒球手套,棒球服,三角网架\n",
      "购买软式棒球,帽子的条件下有100.00%概率买球棒与球棒袋,棒球手套,棒球服,三角网架\n",
      "\n",
      "棒球手套,帽子 → 球棒与球棒袋,棒球服,软式棒球,三角网架\n",
      "购买棒球手套,帽子的条件下有100.00%概率买球棒与球棒袋,棒球服,软式棒球,三角网架\n",
      "\n",
      "三角网架,帽子 → 球棒与球棒袋,棒球手套,棒球服,软式棒球\n",
      "购买三角网架,帽子的条件下有100.00%概率买球棒与球棒袋,棒球手套,棒球服,软式棒球\n",
      "\n",
      "棒球服,软式棒球 → 球棒与球棒袋,棒球手套,三角网架,帽子\n",
      "购买棒球服,软式棒球的条件下有50.00%概率买球棒与球棒袋,棒球手套,三角网架,帽子\n",
      "\n",
      "棒球手套,棒球服 → 球棒与球棒袋,三角网架,软式棒球,帽子\n",
      "购买棒球手套,棒球服的条件下有50.00%概率买球棒与球棒袋,三角网架,软式棒球,帽子\n",
      "\n",
      "棒球服,三角网架 → 球棒与球棒袋,棒球手套,软式棒球,帽子\n",
      "购买棒球服,三角网架的条件下有50.00%概率买球棒与球棒袋,棒球手套,软式棒球,帽子\n",
      "\n",
      "棒球手套,软式棒球 → 球棒与球棒袋,三角网架,棒球服,帽子\n",
      "购买棒球手套,软式棒球的条件下有100.00%概率买球棒与球棒袋,三角网架,棒球服,帽子\n",
      "\n",
      "软式棒球,三角网架 → 球棒与球棒袋,棒球手套,棒球服,帽子\n",
      "购买软式棒球,三角网架的条件下有50.00%概率买球棒与球棒袋,棒球手套,棒球服,帽子\n",
      "\n",
      "棒球手套,三角网架 → 球棒与球棒袋,棒球服,软式棒球,帽子\n",
      "购买棒球手套,三角网架的条件下有50.00%概率买球棒与球棒袋,棒球服,软式棒球,帽子\n",
      "\n",
      "帽子 → 球棒与球棒袋,棒球服,软式棒球,棒球手套,三角网架\n",
      "购买帽子的条件下有100.00%概率买球棒与球棒袋,棒球服,软式棒球,棒球手套,三角网架\n",
      "\n",
      "三角网架 → 球棒与球棒袋,帽子,棒球服,软式棒球,棒球手套\n",
      "购买三角网架的条件下有33.33%概率买球棒与球棒袋,帽子,棒球服,软式棒球,棒球手套\n",
      "\n",
      "----------------------------------------\n",
      "\n",
      "----------------------------------------\n",
      "中部销售大区的强关联规则\n",
      "棒球服 → 硬式棒球\n",
      "购买棒球服的条件下有50.00%概率买硬式棒球\n",
      "\n",
      "硬式棒球 → 棒球服\n",
      "购买硬式棒球的条件下有100.00%概率买棒球服\n",
      "\n",
      "球棒与球棒袋 → 硬式棒球\n",
      "购买球棒与球棒袋的条件下有100.00%概率买硬式棒球\n",
      "\n",
      "硬式棒球 → 球棒与球棒袋\n",
      "购买硬式棒球的条件下有100.00%概率买球棒与球棒袋\n",
      "\n",
      "球棒与球棒袋 → 棒球服\n",
      "购买球棒与球棒袋的条件下有100.00%概率买棒球服\n",
      "\n",
      "棒球服 → 球棒与球棒袋\n",
      "购买棒球服的条件下有50.00%概率买球棒与球棒袋\n",
      "\n",
      "球棒与球棒袋,棒球服 → 硬式棒球\n",
      "购买球棒与球棒袋,棒球服的条件下有100.00%概率买硬式棒球\n",
      "\n",
      "球棒与球棒袋,硬式棒球 → 棒球服\n",
      "购买球棒与球棒袋,硬式棒球的条件下有100.00%概率买棒球服\n",
      "\n",
      "棒球服,硬式棒球 → 球棒与球棒袋\n",
      "购买棒球服,硬式棒球的条件下有100.00%概率买球棒与球棒袋\n",
      "\n",
      "球棒与球棒袋 → 棒球服,硬式棒球\n",
      "购买球棒与球棒袋的条件下有100.00%概率买棒球服,硬式棒球\n",
      "\n",
      "棒球服 → 球棒与球棒袋,硬式棒球\n",
      "购买棒球服的条件下有50.00%概率买球棒与球棒袋,硬式棒球\n",
      "\n",
      "硬式棒球 → 球棒与球棒袋,棒球服\n",
      "购买硬式棒球的条件下有100.00%概率买球棒与球棒袋,棒球服\n",
      "\n",
      "头盔 → 棒球手套\n",
      "购买头盔的条件下有50.00%概率买棒球手套\n",
      "\n",
      "击打手套 → 头盔\n",
      "购买击打手套的条件下有100.00%概率买头盔\n",
      "\n",
      "头盔 → 击打手套\n",
      "购买头盔的条件下有100.00%概率买击打手套\n",
      "\n",
      "击打手套 → 棒球手套\n",
      "购买击打手套的条件下有50.00%概率买棒球手套\n",
      "\n",
      "棒球手套,击打手套 → 头盔\n",
      "购买棒球手套,击打手套的条件下有100.00%概率买头盔\n",
      "\n",
      "棒球手套,头盔 → 击打手套\n",
      "购买棒球手套,头盔的条件下有100.00%概率买击打手套\n",
      "\n",
      "击打手套,头盔 → 棒球手套\n",
      "购买击打手套,头盔的条件下有50.00%概率买棒球手套\n",
      "\n",
      "击打手套 → 棒球手套,头盔\n",
      "购买击打手套的条件下有50.00%概率买棒球手套,头盔\n",
      "\n",
      "头盔 → 棒球手套,击打手套\n",
      "购买头盔的条件下有50.00%概率买棒球手套,击打手套\n",
      "\n",
      "击打手套 → 三角网架\n",
      "购买击打手套的条件下有50.00%概率买三角网架\n",
      "\n",
      "三角网架 → 击打手套\n",
      "购买三角网架的条件下有100.00%概率买击打手套\n",
      "\n",
      "头盔 → 三角网架\n",
      "购买头盔的条件下有50.00%概率买三角网架\n",
      "\n",
      "三角网架 → 头盔\n",
      "购买三角网架的条件下有100.00%概率买头盔\n",
      "\n",
      "击打手套,头盔 → 三角网架\n",
      "购买击打手套,头盔的条件下有50.00%概率买三角网架\n",
      "\n",
      "击打手套,三角网架 → 头盔\n",
      "购买击打手套,三角网架的条件下有100.00%概率买头盔\n",
      "\n",
      "头盔,三角网架 → 击打手套\n",
      "购买头盔,三角网架的条件下有100.00%概率买击打手套\n",
      "\n",
      "击打手套 → 头盔,三角网架\n",
      "购买击打手套的条件下有50.00%概率买头盔,三角网架\n",
      "\n",
      "头盔 → 击打手套,三角网架\n",
      "购买头盔的条件下有50.00%概率买击打手套,三角网架\n",
      "\n",
      "三角网架 → 击打手套,头盔\n",
      "购买三角网架的条件下有100.00%概率买击打手套,头盔\n",
      "\n",
      "----------------------------------------\n",
      "\n",
      "----------------------------------------\n",
      "东北区销售大区的强关联规则\n",
      "棒球手套 → 头盔\n",
      "购买棒球手套的条件下有40.00%概率买头盔\n",
      "\n",
      "头盔 → 棒球手套\n",
      "购买头盔的条件下有50.00%概率买棒球手套\n",
      "\n",
      "硬式棒球 → 头盔\n",
      "购买硬式棒球的条件下有100.00%概率买头盔\n",
      "\n",
      "击打手套 → 头盔\n",
      "购买击打手套的条件下有100.00%概率买头盔\n",
      "\n",
      "击打手套 → 棒球手套\n",
      "购买击打手套的条件下有100.00%概率买棒球手套\n",
      "\n",
      "棒球手套,击打手套 → 头盔\n",
      "购买棒球手套,击打手套的条件下有100.00%概率买头盔\n",
      "\n",
      "棒球手套,头盔 → 击打手套\n",
      "购买棒球手套,头盔的条件下有50.00%概率买击打手套\n",
      "\n",
      "击打手套,头盔 → 棒球手套\n",
      "购买击打手套,头盔的条件下有100.00%概率买棒球手套\n",
      "\n",
      "击打手套 → 棒球手套,头盔\n",
      "购买击打手套的条件下有100.00%概率买棒球手套,头盔\n",
      "\n",
      "软式棒球 → 棒球手套\n",
      "购买软式棒球的条件下有50.00%概率买棒球手套\n",
      "\n",
      "捕手护具 → 头盔\n",
      "购买捕手护具的条件下有100.00%概率买头盔\n",
      "\n",
      "球棒与球棒袋 → 帽子\n",
      "购买球棒与球棒袋的条件下有50.00%概率买帽子\n",
      "\n",
      "帽子 → 球棒与球棒袋\n",
      "购买帽子的条件下有50.00%概率买球棒与球棒袋\n",
      "\n",
      "帽子 → 棒球手套\n",
      "购买帽子的条件下有50.00%概率买棒球手套\n",
      "\n",
      "球棒与球棒袋,棒球手套 → 帽子\n",
      "购买球棒与球棒袋,棒球手套的条件下有100.00%概率买帽子\n",
      "\n",
      "球棒与球棒袋,帽子 → 棒球手套\n",
      "购买球棒与球棒袋,帽子的条件下有100.00%概率买棒球手套\n",
      "\n",
      "棒球手套,帽子 → 球棒与球棒袋\n",
      "购买棒球手套,帽子的条件下有100.00%概率买球棒与球棒袋\n",
      "\n",
      "球棒与球棒袋 → 棒球手套,帽子\n",
      "购买球棒与球棒袋的条件下有50.00%概率买棒球手套,帽子\n",
      "\n",
      "帽子 → 球棒与球棒袋,棒球手套\n",
      "购买帽子的条件下有50.00%概率买球棒与球棒袋,棒球手套\n",
      "\n",
      "球棒与球棒袋 → 棒球手套\n",
      "购买球棒与球棒袋的条件下有50.00%概率买棒球手套\n",
      "\n",
      "装备包 → 球棒与球棒袋\n",
      "购买装备包的条件下有100.00%概率买球棒与球棒袋\n",
      "\n",
      "球棒与球棒袋 → 装备包\n",
      "购买球棒与球棒袋的条件下有50.00%概率买装备包\n",
      "\n",
      "帽子 → 三角网架\n",
      "购买帽子的条件下有50.00%概率买三角网架\n",
      "\n",
      "三角网架 → 帽子\n",
      "购买三角网架的条件下有100.00%概率买帽子\n",
      "\n",
      "球棒与球棒袋 → 三角网架\n",
      "购买球棒与球棒袋的条件下有50.00%概率买三角网架\n",
      "\n",
      "三角网架 → 球棒与球棒袋\n",
      "购买三角网架的条件下有100.00%概率买球棒与球棒袋\n",
      "\n",
      "三角网架 → 棒球手套\n",
      "购买三角网架的条件下有100.00%概率买棒球手套\n",
      "\n",
      "球棒与球棒袋,帽子 → 三角网架\n",
      "购买球棒与球棒袋,帽子的条件下有100.00%概率买三角网架\n",
      "\n",
      "球棒与球棒袋,三角网架 → 帽子\n",
      "购买球棒与球棒袋,三角网架的条件下有100.00%概率买帽子\n",
      "\n",
      "三角网架,帽子 → 球棒与球棒袋\n",
      "购买三角网架,帽子的条件下有100.00%概率买球棒与球棒袋\n",
      "\n",
      "球棒与球棒袋 → 三角网架,帽子\n",
      "购买球棒与球棒袋的条件下有50.00%概率买三角网架,帽子\n",
      "\n",
      "帽子 → 球棒与球棒袋,三角网架\n",
      "购买帽子的条件下有50.00%概率买球棒与球棒袋,三角网架\n",
      "\n",
      "三角网架 → 球棒与球棒袋,帽子\n",
      "购买三角网架的条件下有100.00%概率买球棒与球棒袋,帽子\n",
      "\n",
      "棒球手套,帽子 → 三角网架\n",
      "购买棒球手套,帽子的条件下有100.00%概率买三角网架\n",
      "\n",
      "棒球手套,三角网架 → 帽子\n",
      "购买棒球手套,三角网架的条件下有100.00%概率买帽子\n",
      "\n",
      "三角网架,帽子 → 棒球手套\n",
      "购买三角网架,帽子的条件下有100.00%概率买棒球手套\n",
      "\n",
      "帽子 → 棒球手套,三角网架\n",
      "购买帽子的条件下有50.00%概率买棒球手套,三角网架\n",
      "\n",
      "三角网架 → 棒球手套,帽子\n",
      "购买三角网架的条件下有100.00%概率买棒球手套,帽子\n",
      "\n",
      "球棒与球棒袋,棒球手套 → 三角网架\n",
      "购买球棒与球棒袋,棒球手套的条件下有100.00%概率买三角网架\n",
      "\n",
      "球棒与球棒袋,三角网架 → 棒球手套\n",
      "购买球棒与球棒袋,三角网架的条件下有100.00%概率买棒球手套\n",
      "\n",
      "棒球手套,三角网架 → 球棒与球棒袋\n",
      "购买棒球手套,三角网架的条件下有100.00%概率买球棒与球棒袋\n",
      "\n",
      "球棒与球棒袋 → 棒球手套,三角网架\n",
      "购买球棒与球棒袋的条件下有50.00%概率买棒球手套,三角网架\n",
      "\n",
      "三角网架 → 球棒与球棒袋,棒球手套\n",
      "购买三角网架的条件下有100.00%概率买球棒与球棒袋,棒球手套\n",
      "\n",
      "球棒与球棒袋,棒球手套,帽子 → 三角网架\n",
      "购买球棒与球棒袋,棒球手套,帽子的条件下有100.00%概率买三角网架\n",
      "\n",
      "球棒与球棒袋,棒球手套,三角网架 → 帽子\n",
      "购买球棒与球棒袋,棒球手套,三角网架的条件下有100.00%概率买帽子\n",
      "\n",
      "球棒与球棒袋,三角网架,帽子 → 棒球手套\n",
      "购买球棒与球棒袋,三角网架,帽子的条件下有100.00%概率买棒球手套\n",
      "\n",
      "棒球手套,三角网架,帽子 → 球棒与球棒袋\n",
      "购买棒球手套,三角网架,帽子的条件下有100.00%概率买球棒与球棒袋\n",
      "\n",
      "球棒与球棒袋,棒球手套 → 三角网架,帽子\n",
      "购买球棒与球棒袋,棒球手套的条件下有100.00%概率买三角网架,帽子\n",
      "\n",
      "球棒与球棒袋,帽子 → 棒球手套,三角网架\n",
      "购买球棒与球棒袋,帽子的条件下有100.00%概率买棒球手套,三角网架\n",
      "\n",
      "球棒与球棒袋,三角网架 → 棒球手套,帽子\n",
      "购买球棒与球棒袋,三角网架的条件下有100.00%概率买棒球手套,帽子\n",
      "\n",
      "棒球手套,帽子 → 球棒与球棒袋,三角网架\n",
      "购买棒球手套,帽子的条件下有100.00%概率买球棒与球棒袋,三角网架\n",
      "\n",
      "棒球手套,三角网架 → 球棒与球棒袋,帽子\n",
      "购买棒球手套,三角网架的条件下有100.00%概率买球棒与球棒袋,帽子\n",
      "\n",
      "三角网架,帽子 → 球棒与球棒袋,棒球手套\n",
      "购买三角网架,帽子的条件下有100.00%概率买球棒与球棒袋,棒球手套\n",
      "\n",
      "球棒与球棒袋 → 棒球手套,三角网架,帽子\n",
      "购买球棒与球棒袋的条件下有50.00%概率买棒球手套,三角网架,帽子\n",
      "\n",
      "帽子 → 球棒与球棒袋,棒球手套,三角网架\n",
      "购买帽子的条件下有50.00%概率买球棒与球棒袋,棒球手套,三角网架\n",
      "\n",
      "三角网架 → 球棒与球棒袋,棒球手套,帽子\n",
      "购买三角网架的条件下有100.00%概率买球棒与球棒袋,棒球手套,帽子\n",
      "\n",
      "----------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for _region in data['销售大区'].unique():\n",
    "    print('-'*40)\n",
    "    print(_region+'销售大区的强关联规则')\n",
    "    region_data = data[data['销售大区']==_region]\n",
    "    region_data = region_data.values[:,-1]\n",
    "    TE = TransactionEncoder()\n",
    "    e_region = TE.fit_transform(region_data)\n",
    "    e_region = pd.DataFrame(e_region, columns=TE.columns_)\n",
    "    items = fpgrowth(e_region, min_support=0.03, use_colnames=True)\n",
    "    rules = association_rules(items, min_threshold=0.3)\n",
    "    print_rule(rules)\n",
    "    print('-'*40)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
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